From 9ecbedd1446970cc1ddc6e4d88b5a355ecc7d013 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Sun, 3 Mar 2019 10:33:56 -0800 Subject: [PATCH 01/31] Experiment with version-number GitHub dependency --- DESCRIPTION | 2 +- R/zzz.R | 24 ++++++++---------------- 2 files changed, 9 insertions(+), 17 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 197ed31..40432e2 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -35,11 +35,11 @@ Imports: TMBhelper Depends: R (>= 3.1.0), - FishStatsUtils Suggests: testthat, knitr, rmarkdown, + FishStatsUtils, R.rsp Remotes: james-thorson/FishStatsUtils, diff --git a/R/zzz.R b/R/zzz.R index 6dfac42..f75b609 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -15,22 +15,14 @@ #utils::install.packages("INLA", repos="https://www.math.ntnu.no/inla/R/stable") utils::install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE) } - #if( !"SpatialDeltaGLMM"%in%utils::installed.packages()[,1] || utils::packageVersion("SpatialDeltaGLMM")<3.40 ){ - # packageStartupMessage("Installing package: SpatialDeltaGLMM...") - # devtools::install_github("nwfsc-assess/geostatistical_delta-GLMM") - #} - #if( !"SpatialDFA"%in%utils::installed.packages()[,1] ){ - # packageStartupMessage("Installing package: SpatialDFA...") - # devtools::install_github("james-thorson/spatial_DFA") - #} - #if( !"ThorsonUtilities"%in%utils::installed.packages()[,1] ){ - # packageStartupMessage("Installing package: ThorsonUtilities...") - # devtools::install_github("james-thorson/utilities") - #} - #if( !"TMBhelper"%in%utils::installed.packages()[,1] ){ - # packageStartupMessage("Installing package: TMBhelper...") - # devtools::install_github("kaskr/TMB_contrib_R/TMBhelper") - #} + + # Load `FishStatsUtils` via .onAttach because importFrom wasn't working + # Also requries moving FishStatsUtils to SUGGESTS, so that it doesn't isntall main branch + if( utils::packageVersion("FishStatsUtils") < numeric_version("1.1.0") ){ + packageStartupMessage("Updating package FishStatsUtils because previously using version < 1.1.0") + devtools::install_github("james-thorson/FishStatsUtils", ref="development") + } + packageStartupMessage( "Loading package `FishStatsUtils`" ) } #' Copy of VAST::make_model From 74f52904afc39c12cb3bd1b3cd2258e2d9ba2317 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Sun, 3 Mar 2019 10:35:06 -0800 Subject: [PATCH 02/31] small fix to last commit --- R/zzz.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/zzz.R b/R/zzz.R index f75b609..eb6b54d 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -18,7 +18,7 @@ # Load `FishStatsUtils` via .onAttach because importFrom wasn't working # Also requries moving FishStatsUtils to SUGGESTS, so that it doesn't isntall main branch - if( utils::packageVersion("FishStatsUtils") < numeric_version("1.1.0") ){ + if( !"FishStatsUtils" %in% utils::installed.packages()[,1] || utils::packageVersion("FishStatsUtils") < numeric_version("1.1.0") ){ packageStartupMessage("Updating package FishStatsUtils because previously using version < 1.1.0") devtools::install_github("james-thorson/FishStatsUtils", ref="development") } From a93500ae87ae8c8d501e3f3047f509c13389b147 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Sun, 3 Mar 2019 10:40:06 -0800 Subject: [PATCH 03/31] Fix to last commit --- R/zzz.R | 1 + 1 file changed, 1 insertion(+) diff --git a/R/zzz.R b/R/zzz.R index eb6b54d..3c9ffc8 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -23,6 +23,7 @@ devtools::install_github("james-thorson/FishStatsUtils", ref="development") } packageStartupMessage( "Loading package `FishStatsUtils`" ) + library(FishStatsUtils) } #' Copy of VAST::make_model From 41dfdc1df527d2b1b86bf8e787266b8f38a679cf Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Sun, 3 Mar 2019 10:49:37 -0800 Subject: [PATCH 04/31] Simplify Version checks ... ... using semantic_version logical operators --- R/make_data.R | 14 +++++++------- R/make_model.R | 8 +++++--- 2 files changed, 12 insertions(+), 10 deletions(-) diff --git a/R/make_data.R b/R/make_data.R index b69feb9..c263960 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -279,36 +279,36 @@ function( b_i, a_i, c_iz, s_i, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), if( any(ObsModel_ez[,2]==1) ){ # Versions 1.6.0 through 2.2.0 used a previous interpretation of area-swept for Poisson-link model and are not consistent with Q-Q plotting - if( Version%in%c("VAST_v2_2_0","VAST_v2_1_0","VAST_v2_0_0","VAST_v1_9_0","VAST_v1_8_0","VAST_v1_7_0","VAST_v1_6_0") ){ + if( convert_version_name(Version) <= convert_version_name("VAST_v2_2_0") ){ stop("Problem with Poisson-link model for VAST versions 1.6.0 through 2.2.0") } # Versions 1.0.0 through 1.5.0 don't have Poisson-link model - if( Version%in%c("VAST_v1_5_0","VAST_v1_4_0","VAST_v1_3_0","VAST_v1_2_0","VAST_v1_1_0","VAST_v1_0_0") ){ + if( convert_version_name(Version) <= convert_version_name("VAST_v1_5_0") ){ stop("Poisson-link model for VAST versions 1.0.0 through 1.5.0") } # Can't use multiple error distributions prior to version 3.0.0 - if( Version%in%c("VAST_v2_8_0","VAST_v2_7_0","VAST_v2_6_0","VAST_v2_5_0","VAST_v2_4_0","VAST_v2_3_0","VAST_v2_2_0","VAST_v2_1_0","VAST_v2_0_0","VAST_v1_9_0","VAST_v1_8_0","VAST_v1_7_0","VAST_v1_6_0","VAST_v1_5_0","VAST_v1_4_0","VAST_v1_3_0","VAST_v1_2_0","VAST_v1_1_0","VAST_v1_0_0") ){ + if( convert_version_name(Version) <= convert_version_name("VAST_v2_8_0") ){ if( length(unique(ObsModel_ez[,1]))>1 ) stop("Can't use multiple error distributions prior to version 3.0.0") } } if( any(FieldConfig_input == -2) ){ # Versions 2.6.0 was the first to allow "IID" setting for FieldConfig_input elements - if( Version%in%c("VAST_v2_5_0","VAST_v2_4_0","VAST_v2_3_0","VAST_v2_2_0","VAST_v2_1_0","VAST_v2_0_0","VAST_v1_9_0","VAST_v1_8_0","VAST_v1_7_0","VAST_v1_6_0","VAST_v1_5_0","VAST_v1_4_0","VAST_v1_3_0","VAST_v1_2_0","VAST_v1_1_0","VAST_v1_0_0") ){ + if( convert_version_name(Version) <= convert_version_name("VAST_v2_5_0") ){ stop("Problem with 'IID' option for factors for VAST versions 1.0.0 through 2.6.0") } } if( ncol(c_iz)>1 ){ - if( Version%in%c("VAST_v3_0_0","VAST_v2_8_0","VAST_v2_7_0","VAST_v2_6_0","VAST_v2_5_0","VAST_v2_4_0","VAST_v2_3_0","VAST_v2_2_0","VAST_v2_1_0","VAST_v2_0_0","VAST_v1_9_0","VAST_v1_8_0","VAST_v1_7_0","VAST_v1_6_0","VAST_v1_5_0","VAST_v1_4_0","VAST_v1_3_0","VAST_v1_2_0","VAST_v1_1_0","VAST_v1_0_0") ){ + if( convert_version_name(Version) <= convert_version_name("VAST_v3_0_0") ){ stop("Can't have observations assigned to more than one category prior to version 4.0.0") } } if( any(RhoConfig[1:2]==3) ){ - if( Version%in%c("VAST_v4_1_0","VAST_v4_0_0","VAST_v3_0_0","VAST_v2_8_0","VAST_v2_7_0","VAST_v2_6_0","VAST_v2_5_0","VAST_v2_4_0","VAST_v2_3_0","VAST_v2_2_0","VAST_v2_1_0","VAST_v2_0_0","VAST_v1_9_0","VAST_v1_8_0","VAST_v1_7_0","VAST_v1_6_0","VAST_v1_5_0","VAST_v1_4_0","VAST_v1_3_0","VAST_v1_2_0","VAST_v1_1_0","VAST_v1_0_0") ){ + if( convert_version_name(Version) <= convert_version_name("VAST_v4_1_0") ){ stop("There was bug in specifying fixed intercepts among years for versions prior to V4.2.0") } } if( Options2use['SD_observation_density']==1 ){ - if( Version%in%c("VAST_v4_1_0","VAST_v4_0_0","VAST_v3_0_0","VAST_v2_8_0","VAST_v2_7_0","VAST_v2_6_0","VAST_v2_5_0","VAST_v2_4_0","VAST_v2_3_0","VAST_v2_2_0","VAST_v2_1_0","VAST_v2_0_0","VAST_v1_9_0","VAST_v1_8_0","VAST_v1_7_0","VAST_v1_6_0","VAST_v1_5_0","VAST_v1_4_0","VAST_v1_3_0","VAST_v1_2_0","VAST_v1_1_0","VAST_v1_0_0") ){ + if( convert_version_name(Version) <= convert_version_name("VAST_v4_1_0") ){ stop("Calculating 'SD_observation_density' is not possible prior to V4.2.0") } } diff --git a/R/make_model.R b/R/make_model.R index 9582a4f..766b20f 100644 --- a/R/make_model.R +++ b/R/make_model.R @@ -121,9 +121,11 @@ function( TmbData, Version, RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilo Obj$control <- list(parscale=1, REPORT=1, reltol=1e-12, maxit=100) # Add normalization in - if(Version %in% c("VAST_v7_0_0","VAST_v6_0_0","VAST_v5_5_0","VAST_v5_4_0","VAST_v5_3_0","VAST_v5_2_0","VAST_v5_1_0","VAST_v5_0_0","VAST_v4_4_0","VAST_v4_3_0","VAST_v4_2_0","VAST_v4_1_0") & Options['normalize_GMRF_in_CPP']==FALSE ){ - message("Normalizing GMRF in R using `TMB::normalize` feature") - Obj = TMB::normalize(Obj, flag="include_data", value=FALSE) + if( FishStatsUtils::convert_version_name(Version) >= FishStatsUtils::convert_version_name("VAST_v4_1_0") ){ + if( Options['normalize_GMRF_in_CPP']==FALSE ){ + message("Normalizing GMRF in R using `TMB::normalize` feature") + Obj = TMB::normalize(Obj, flag="include_data", value=FALSE) + } } # Diagnostic functions (optional) From 6fc999e9ed8f49289d23a20d777dc5a8e828eff8 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Sun, 3 Mar 2019 12:00:10 -0800 Subject: [PATCH 05/31] Simplify version checks take2 --- R/make_data.R | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/R/make_data.R b/R/make_data.R index c263960..4e15db9 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -279,36 +279,36 @@ function( b_i, a_i, c_iz, s_i, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), if( any(ObsModel_ez[,2]==1) ){ # Versions 1.6.0 through 2.2.0 used a previous interpretation of area-swept for Poisson-link model and are not consistent with Q-Q plotting - if( convert_version_name(Version) <= convert_version_name("VAST_v2_2_0") ){ + if( FishStatsUtils::convert_version_name(Version) <= FishStatsUtils::convert_version_name("VAST_v2_2_0") ){ stop("Problem with Poisson-link model for VAST versions 1.6.0 through 2.2.0") } # Versions 1.0.0 through 1.5.0 don't have Poisson-link model - if( convert_version_name(Version) <= convert_version_name("VAST_v1_5_0") ){ + if( FishStatsUtils::convert_version_name(Version) <= FishStatsUtils::convert_version_name("VAST_v1_5_0") ){ stop("Poisson-link model for VAST versions 1.0.0 through 1.5.0") } # Can't use multiple error distributions prior to version 3.0.0 - if( convert_version_name(Version) <= convert_version_name("VAST_v2_8_0") ){ + if( FishStatsUtils::convert_version_name(Version) <= FishStatsUtils::convert_version_name("VAST_v2_8_0") ){ if( length(unique(ObsModel_ez[,1]))>1 ) stop("Can't use multiple error distributions prior to version 3.0.0") } } if( any(FieldConfig_input == -2) ){ # Versions 2.6.0 was the first to allow "IID" setting for FieldConfig_input elements - if( convert_version_name(Version) <= convert_version_name("VAST_v2_5_0") ){ + if( FishStatsUtils::convert_version_name(Version) <= FishStatsUtils::convert_version_name("VAST_v2_5_0") ){ stop("Problem with 'IID' option for factors for VAST versions 1.0.0 through 2.6.0") } } if( ncol(c_iz)>1 ){ - if( convert_version_name(Version) <= convert_version_name("VAST_v3_0_0") ){ + if( FishStatsUtils::convert_version_name(Version) <= FishStatsUtils::convert_version_name("VAST_v3_0_0") ){ stop("Can't have observations assigned to more than one category prior to version 4.0.0") } } if( any(RhoConfig[1:2]==3) ){ - if( convert_version_name(Version) <= convert_version_name("VAST_v4_1_0") ){ + if( FishStatsUtils::convert_version_name(Version) <= FishStatsUtils::convert_version_name("VAST_v4_1_0") ){ stop("There was bug in specifying fixed intercepts among years for versions prior to V4.2.0") } } if( Options2use['SD_observation_density']==1 ){ - if( convert_version_name(Version) <= convert_version_name("VAST_v4_1_0") ){ + if( FishStatsUtils::convert_version_name(Version) <= FishStatsUtils::convert_version_name("VAST_v4_1_0") ){ stop("Calculating 'SD_observation_density' is not possible prior to V4.2.0") } } From d566c91bd3709ab7714ea3cebfb7e18a52b6c02a Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Mon, 4 Mar 2019 15:38:48 -0800 Subject: [PATCH 06/31] Initial commit for V8.0.0... ... which adds `fine_scale` for fine-scale covariates and fine-scale interpolation of GMRFs --- R/Make_Map.R | 12 +- R/Param_Fn.R | 3 +- R/make_data.R | 98 +- inst/executables/VAST_v8_0_0.cpp | 2013 ++++++++++++++++++++++++++++++ 4 files changed, 2095 insertions(+), 31 deletions(-) create mode 100644 inst/executables/VAST_v8_0_0.cpp diff --git a/R/Make_Map.R b/R/Make_Map.R index 6d31697..56ab09b 100644 --- a/R/Make_Map.R +++ b/R/Make_Map.R @@ -262,9 +262,15 @@ function( DataList, TmbParams, RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Eps Map[["lambda2_k"]] = factor(Map[["lambda2_k"]]) # Dynamic covariates - if( "X_xtp" %in% names(DataList) ){ - Var_p = apply( DataList[["X_xtp"]], MARGIN=3, FUN=function(array){var(as.vector(array))}) - Var_tp = apply( DataList[["X_xtp"]], MARGIN=2:3, FUN=var ) + if( any(c("X_xtp","X_itp") %in% names(DataList)) ){ + if( "X_xtp" %in% names(DataList) ){ + Var_p = apply( DataList[["X_xtp"]], MARGIN=3, FUN=function(array){var(as.vector(array))}) + Var_tp = apply( DataList[["X_xtp"]], MARGIN=2:3, FUN=var ) + } + if( "X_itp" %in% names(DataList) ){ + Var_p = apply( DataList[["X_itp"]], MARGIN=3, FUN=function(array){var(as.vector(array))}) + Var_tp = apply( DataList[["X_itp"]], MARGIN=2:3, FUN=var ) + } if( "gamma1_tp" %in% names(TmbParams) ){ Map[["gamma1_tp"]] = Map[["gamma2_tp"]] = matrix( 1:(DataList$n_t*DataList$n_p), nrow=DataList$n_t, ncol=DataList$n_p ) # By default: diff --git a/R/Param_Fn.R b/R/Param_Fn.R index fe5b264..c6495c0 100644 --- a/R/Param_Fn.R +++ b/R/Param_Fn.R @@ -116,14 +116,13 @@ function( Version, DataList, RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsil if(Version%in%c("VAST_v5_3_0")){ Return = list("ln_H_input"=c(0,0), "Chi_fr"=rarray(dim=c(max(DataList$FieldConfig[2,1],1),DataList$VamConfig[2])), "Psi_fr"=rarray(dim=c(max(DataList$FieldConfig[2,1],1),DataList$VamConfig[2])), "beta1_ct"=NA, "gamma1_j"=rep(0,ncol(DataList$X_xj)), "gamma1_ctp"=array(0,dim=c(DataList$n_c,DataList$n_t,DataList$n_p)), "lambda1_k"=rep(0,ncol(DataList$Q_ik)), "L1_z"=NA, "L_omega1_z"=NA, "L_epsilon1_z"=NA, "logkappa1"=log(0.9), "Beta_mean1"=0, "logsigmaB1"=log(1), "Beta_rho1"=0, "Epsilon_rho1_f"=NA, "log_sigmaratio1_z"=rep(0,ncol(DataList$t_iz)), "eta1_vf"=NA, "Omegainput1_sf"=NA, "Epsiloninput1_sft"=NA, "beta2_ct"=NA, "gamma2_j"=rep(0,ncol(DataList$X_xj)), "gamma2_ctp"=array(0,dim=c(DataList$n_c,DataList$n_t,DataList$n_p)), "lambda2_k"=rep(0,ncol(DataList$Q_ik)), "L2_z"=NA, "L_omega2_z"=NA, "L_epsilon2_z"=NA, "logkappa2"=log(0.9), "Beta_mean2"=0, "logsigmaB2"=log(1), "Beta_rho2"=0, "Epsilon_rho2_f"=NA, "log_sigmaratio2_z"=rep(0,ncol(DataList$t_iz)), "logSigmaM"=rep(1,DataList$n_e)%o%c(log(5),log(2),log(1)), "delta_i"=rnorm(n=ifelse(any(DataList$ObsModel_ez[,1]%in%c(11,14)),DataList$n_i,1),sd=0.1), "eta2_vf"=NA, "Omegainput2_sf"=NA, "Epsiloninput2_sft"=NA ) } - if(Version%in%c("VAST_v5_5_0","VAST_v5_4_0")){ Return = list("ln_H_input"=c(0,0), "Chi_fr"=rarray(dim=c(max(DataList$FieldConfig[2,1],1),DataList$VamConfig[2])), "Psi_fr"=rarray(dim=c(max(DataList$FieldConfig[2,1],1),DataList$VamConfig[2])), "beta1_ct"=NA, "gamma1_j"=rep(0,ncol(DataList$X_xj)), "gamma1_ctp"=array(0,dim=c(DataList$n_c,DataList$n_t,DataList$n_p)), "lambda1_k"=rep(0,ncol(DataList$Q_ik)), "L1_z"=NA, "L_omega1_z"=NA, "L_epsilon1_z"=NA, "logkappa1"=log(0.9), "Beta_mean1_c"=rep(0,DataList$n_c), "logsigmaB1_c"=rep(log(1),DataList$n_c), "Beta_rho1_c"=rep(0,DataList$n_c), "Epsilon_rho1_f"=NA, "log_sigmaratio1_z"=rep(0,ncol(DataList$t_iz)), "eta1_vf"=NA, "Omegainput1_sf"=NA, "Epsiloninput1_sft"=NA, "beta2_ct"=NA, "gamma2_j"=rep(0,ncol(DataList$X_xj)), "gamma2_ctp"=array(0,dim=c(DataList$n_c,DataList$n_t,DataList$n_p)), "lambda2_k"=rep(0,ncol(DataList$Q_ik)), "L2_z"=NA, "L_omega2_z"=NA, "L_epsilon2_z"=NA, "logkappa2"=log(0.9), "Beta_mean2_c"=rep(0,DataList$n_c), "logsigmaB2_c"=rep(log(1),DataList$n_c), "Beta_rho2_c"=rep(0,DataList$n_c), "Epsilon_rho2_f"=NA, "log_sigmaratio2_z"=rep(0,ncol(DataList$t_iz)), "logSigmaM"=rep(1,DataList$n_e)%o%c(log(5),log(2),log(1)), "delta_i"=rnorm(n=ifelse(any(DataList$ObsModel_ez[,1]%in%c(11,14)),DataList$n_i,1),sd=0.1), "eta2_vf"=NA, "Omegainput2_sf"=NA, "Epsiloninput2_sft"=NA ) } if(Version%in%c("VAST_v6_0_0")){ Return = list("ln_H_input"=c(0,0), "Chi_fr"=rarray(dim=c(max(DataList$FieldConfig[2,1],1),DataList$VamConfig[2])), "Psi_fr"=rarray(dim=c(max(DataList$FieldConfig[2,1],1),DataList$VamConfig[2])), "beta1_ct"=NA, "gamma1_ctp"=array(0,dim=c(DataList$n_c,DataList$n_t,DataList$n_p)), "lambda1_k"=rep(0,ncol(DataList$Q_ik)), "L1_z"=NA, "L_omega1_z"=NA, "L_epsilon1_z"=NA, "logkappa1"=log(0.9), "Beta_mean1_c"=rep(0,DataList$n_c), "logsigmaB1_c"=rep(log(1),DataList$n_c), "Beta_rho1_c"=rep(0,DataList$n_c), "Epsilon_rho1_f"=NA, "log_sigmaXi1_cp"=array(0,dim=c(DataList$n_c,DataList$n_p)), "log_sigmaratio1_z"=rep(0,ncol(DataList$t_iz)), "eta1_vf"=NA, "Xiinput1_scp"=array(0,dim=c(DataList$n_s,DataList$n_c,DataList$n_p)), "Omegainput1_sf"=NA, "Epsiloninput1_sft"=NA, "beta2_ct"=NA, "gamma2_ctp"=array(0,dim=c(DataList$n_c,DataList$n_t,DataList$n_p)), "lambda2_k"=rep(0,ncol(DataList$Q_ik)), "L2_z"=NA, "L_omega2_z"=NA, "L_epsilon2_z"=NA, "logkappa2"=log(0.9), "Beta_mean2_c"=rep(0,DataList$n_c), "logsigmaB2_c"=rep(log(1),DataList$n_c), "Beta_rho2_c"=rep(0,DataList$n_c), "Epsilon_rho2_f"=NA, "log_sigmaXi2_cp"=array(0,dim=c(DataList$n_c,DataList$n_p)), "log_sigmaratio2_z"=rep(0,ncol(DataList$t_iz)), "logSigmaM"=rep(1,DataList$n_e)%o%c(log(5),log(2),log(1)), "delta_i"=rnorm(n=ifelse(any(DataList$ObsModel_ez[,1]%in%c(11,14)),DataList$n_i,1),sd=0.1), "eta2_vf"=NA, "Xiinput2_scp"=rarray(0,dim=c(DataList$n_s,DataList$n_c,DataList$n_p)), "Omegainput2_sf"=NA, "Epsiloninput2_sft"=NA ) } - if(Version%in%c("VAST_v7_0_0")){ + if(Version%in%c("VAST_v8_0_0","VAST_v7_0_0")){ Return = list("ln_H_input"=c(0,0), "Chi_fr"=rarray(dim=c(max(DataList$FieldConfig[2,1],1),DataList$VamConfig[2])), "Psi_fr"=rarray(dim=c(max(DataList$FieldConfig[2,1],1),DataList$VamConfig[2])), "beta1_ft"=NA, "gamma1_ctp"=array(0,dim=c(DataList$n_c,DataList$n_t,DataList$n_p)), "lambda1_k"=rep(0,ncol(DataList$Q_ik)), "L1_z"=NA, "L_omega1_z"=NA, "L_epsilon1_z"=NA, "L_beta1_z"=NA, "logkappa1"=log(0.9), "Beta_mean1_c"=rep(0,DataList$n_c), "Beta_rho1_f"=NA, "Epsilon_rho1_f"=NA, "log_sigmaXi1_cp"=array(0,dim=c(DataList$n_c,DataList$n_p)), "log_sigmaratio1_z"=rep(0,ncol(DataList$t_iz)), "eta1_vf"=NA, "Xiinput1_scp"=array(0,dim=c(DataList$n_s,DataList$n_c,DataList$n_p)), "Omegainput1_sf"=NA, "Epsiloninput1_sft"=NA, "beta2_ft"=NA, "gamma2_ctp"=array(0,dim=c(DataList$n_c,DataList$n_t,DataList$n_p)), "lambda2_k"=rep(0,ncol(DataList$Q_ik)), "L2_z"=NA, "L_omega2_z"=NA, "L_epsilon2_z"=NA, "L_beta2_z"=NA, "logkappa2"=log(0.9), "Beta_mean2_c"=rep(0,DataList$n_c), "Beta_rho2_f"=NA, "Epsilon_rho2_f"=NA, "log_sigmaXi2_cp"=array(0,dim=c(DataList$n_c,DataList$n_p)), "log_sigmaratio2_z"=rep(0,ncol(DataList$t_iz)), "logSigmaM"=rep(1,DataList$n_e)%o%c(log(5),log(2),log(1)), "delta_i"=rnorm(n=ifelse(any(DataList$ObsModel_ez[,1]%in%c(11,14)),DataList$n_i,1),sd=0.1), "eta2_vf"=NA, "Xiinput2_scp"=rarray(0,dim=c(DataList$n_s,DataList$n_c,DataList$n_p)), "Omegainput2_sf"=NA, "Epsiloninput2_sft"=NA ) } diff --git a/R/make_data.R b/R/make_data.R index 4e15db9..7676702 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -36,10 +36,13 @@ #' } #' @param RhoConfig OPTIONAL, vector of form c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0) specifying whether either intercepts (Beta1 and Beta2) or spatio-temporal variation (Epsilon1 and Epsilon2) is structured among time intervals (0: each year as fixed effect; 1: each year as random following IID distribution; 2: each year as random following a random walk; 3: constant among years as fixed effect; 4: each year as random following AR1 process) #' @param VamConfig Options to estimate interactions, where first slot selects method for forming interaction matrix, the second indicates rank, the third indicates whether to incorporate effect of \code{F_ct}, and the fourth indicates whether to add a new "t=0" year (while incrementing all \code{t_i} inputs) which represents B0 +#' @param spatial_list tagged list of locatoinal information from , i.e., from \code{FishStatsUtils::make_spatial_info} + #' @param a_xl Area associated with each knot #' @param MeshList, tagged list representing location information for the SPDE mesh hyperdistribution, i.e., from \code{SpatialDeltaGLMM::Spatial_Information_Fn} #' @param GridList, tagged list representing location information for the 2D AR1 grid hyperdistribution, i.e., from \code{SpatialDeltaGLMM::Spatial_Information_Fn} #' @param Method, character (either "Mesh" or "Grid") specifying hyperdistribution (Default="Mesh") + #' @param PredTF_i OPTIONAL, whether each observation i is included in the likelihood (PredTF_i[i]=0) or in the predictive probability (PredTF_i[i]=1) #' @param X_xtp OPTIONAL, array of dynamic (varying among time intervals) density covariates #' @param Xconfig_zcp OPTIONAL, 3D array of settings for each dynamic density covariate, where the first dimension corresponds to 1st or 2nd linear predictors, second dimension corresponds to model category, and third dimension corresponds to each density covariate @@ -61,11 +64,13 @@ #' @export make_data <- -function( b_i, a_i, c_iz, s_i, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), +function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), Version, FieldConfig, OverdispersionConfig=c("eta1"=0,"eta2"=0), ObsModel_ez=c("PosDist"=1,"Link"=0), RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0), VamConfig=c("Method"=0,"Rank"=0,"Timing"=0), - a_xl, MeshList, GridList, Method, Aniso=TRUE, PredTF_i=rep(0,length(b_i)), - X_xj=NULL, X_xtp=NULL, Xconfig_zcp=NULL, Q_ik=NULL, Network_sz=NULL, F_ct=NULL, F_init=1, + spatial_list=NULL, a_xl=NULL, MeshList=spatial_list$MeshList, GridList=spatial_list$GridList, + Method=spatial_list$Method, s_i=spatial_list$knot_i-1, Aniso=TRUE, PredTF_i=rep(0,length(b_i)), + X_xj=NULL, X_xtp=NULL, Xconfig_zcp=NULL, X_gtp=NULL, X_itp=NULL, + Q_ik=NULL, Network_sz=NULL, F_ct=NULL, F_init=1, t_yz=NULL, CheckForErrors=TRUE, yearbounds_zz=NULL, Options=c('SD_site_logdensity'=0,'Calculate_Range'=0,'Calculate_effective_area'=0,'Calculate_Cov_SE'=0,'Calculate_Synchrony'=0,'Calculate_proportion'=0), Expansion_cz=NULL ){ @@ -82,6 +87,24 @@ function( b_i, a_i, c_iz, s_i, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), } } + # Check for backwards-compatibility issues + if( is.null(a_xl) ){ + if( !is.null(spatial_list$a_gl) ){ + a_gl = a_xl = spatial_list$a_gl + }else{ + stop("Must provide `a_gl` and/or `a_gl`") + } + }else{ + a_gl = a_xl + warning("Please consider using input `a_gl`; `a_xl` is still available for backwards compatiblity but only `a_gl` will be used") + } + if( FishStatsUtils::convert_version_name(Version) >= FishStatsUtils::convert_version_name("VAST_v8_0_0") ){ + if( !is.null(X_xtp) ) stop("`X_xtp` is not used in version >= 8.0.0") + } + if( FishStatsUtils::convert_version_name(Version) <= FishStatsUtils::convert_version_name("VAST_v7_0_0") ){ + if( !is.null(X_gtp) | !is.null(X_itp) ) stop("`X_gtp` and `X_itp` are not used in version <= 7.0.0") + } + # Adds intercept defaults to FieldConfig if missing if( is.vector(FieldConfig) && length(FieldConfig)==4 ){ FieldConfig = rbind( matrix(FieldConfig,ncol=2,dimnames=list(c("Omega","Epsilon"),c("Component_1","Component_2"))), "Beta"=c("Beta1"="IID","Beta2"="IID") ) @@ -114,8 +137,9 @@ function( b_i, a_i, c_iz, s_i, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), n_e = max(e_i) + 1 n_v = length(unique(v_i)) # If n_v=1, then turn off overdispersion later n_i = length(b_i) - n_x = nrow(a_xl) - n_l = ncol(a_xl) + n_x = nrow(a_gl) + n_l = ncol(a_gl) + n_g = ifelse( is.null(spatial_list), 1, spatial_list$n_g ) # Coerce ObsModel_ez to be a matrix if( !is.matrix(ObsModel_ez) ) ObsModel_ez = matrix( ObsModel_ez, ncol=2, nrow=n_e, byrow=TRUE ) @@ -135,6 +159,20 @@ function( b_i, a_i, c_iz, s_i, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), stop("`X_xtp` has wrong dimensions") } } + if( is.null(X_gtp) ){ + X_gtp = array(0, dim=c(n_g,n_t,1)) + }else{ + if( !is.array(X_gtp) || !(all(dim(X_gtp)[1:2]==c(n_g,n_t))) ){ + stop("`X_gtp` has wrong dimensions") + } + } + if( is.null(X_itp) ){ + X_itp = array(0, dim=c(n_i,n_t,1)) + }else{ + if( !is.array(X_itp) || !(all(dim(X_itp)[1:2]==c(n_i,n_t))) ){ + stop("`X_itp` has wrong dimensions") + } + } if( is.null(Q_ik) ){ Q_ik = matrix(0, nrow=n_i, ncol=1) }else{ @@ -212,10 +250,12 @@ function( b_i, a_i, c_iz, s_i, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), # by default, add nothing as Z_xl if( Options2use['Calculate_Range']==FALSE ){ - Z_xm = matrix(0, nrow=nrow(a_xl), ncol=ncol(a_xl) ) # Size so that it works for Version 3g-3j + Z_gm = Z_xm = matrix(0, nrow=nrow(a_gl), ncol=ncol(a_gl) ) # Size so that it works for Version 3g-3j + }else{ Z_xm = MeshList$loc_x - message( "Calculating range shift for stratum #1:",colnames(a_xl[1])) + Z_gm = spatial_list$loc_g + message( "Calculating range shift for stratum #1:",colnames(a_gl[1])) } ################### @@ -224,7 +264,8 @@ function( b_i, a_i, c_iz, s_i, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), if( CheckForErrors==TRUE ){ if( ncol(ObsModel_ez)!=2 | nrow(ObsModel_ez)!=n_e ) stop("Wrong dimensions for ObsModel_ez") - if( !is.matrix(a_xl) | !is.matrix(X_xj) | !is.matrix(Q_ik) ) stop("a_xl, X_xj, and Q_ik should be matrices") + if( !is.matrix(a_gl) | !is.matrix(X_xj) | !is.matrix(Q_ik) ) stop("a_gl, X_xj, and Q_ik should be matrices") + if( !is.array(X_xtp) | !is.array(X_gtp) | !is.array(X_itp) ) stop( "X_xtp, X_gtp, and X_itp should be matrices") if( (max(s_i)-1)>n_x | min(s_i)<0 ) stop("s_i exceeds bounds in MeshList") if( any(a_i<=0) ) stop("a_i must be greater than zero for all observations, and at least one value of a_i is not") # Warnings about all positive or zero @@ -259,13 +300,15 @@ function( b_i, a_i, c_iz, s_i, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), if( !(all(ObsModel_ez[,2] %in% c(0,1,2,3,4))) ) stop("Please check `ObsModel_ez[,2]` input") if( !all(RhoConfig[1]%in%c(0,1,2,3,4)) | !all(RhoConfig[2]%in%c(0,1,2,3,4,6)) | !all(RhoConfig[3]%in%c(0,1,2,4,5)) | !all(RhoConfig[4]%in%c(0,1,2,4,5,6)) ) stop("Check `RhoConfig` inputs") if( any(is.na(X_xtp)) ) stop("Some `X_xtp` is NA, and this is not allowed") + if( any(is.na(X_gtp)) ) stop("Some `X_gtp` is NA, and this is not allowed") + if( any(is.na(X_itp)) ) stop("Some `X_itp` is NA, and this is not allowed") if( n_c==1 && !all(FieldConfig_input %in% c(-2,-1,1)) ) stop("If using a univariate model, `FieldConfig` must be 0, 1, or `IID` for all entries") } # Check for wrong dimensions if( CheckForErrors==TRUE ){ if( any(c(length(b_i),length(a_i),nrow(c_iz),length(s_i),nrow(tprime_iz),length(v_i),length(PredTF_i))!=n_i) ) stop("b_i, a_i, c_i, s_i, v_i, or tprime_i doesn't have length n_i") - if( nrow(a_xl)!=n_x | ncol(a_xl)!=n_l ) stop("a_xl has wrong dimensions") + if( nrow(a_gl)!=n_x | ncol(a_gl)!=n_l ) stop("a_xl has wrong dimensions") if( nrow(X_xj)!=n_x | ncol(X_xj)!=n_j ) stop("X_xj has wrong dimensions") if( nrow(Q_ik)!=n_i | ncol(Q_ik)!=n_k ) stop("Q_ik has wrong dimensions") if( dim(X_xtp)[1]!=n_x | dim(X_xtp)[2]!=n_t | dim(X_xtp)[3]!=n_p ) stop("X_xtp has wrong dimensions") @@ -462,55 +505,58 @@ function( b_i, a_i, c_iz, s_i, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), Options_vec = c("Aniso"=Aniso, "R2_interpretation"=0, "Rho_beta1_TF"=ifelse(RhoConfig[1]%in%c(1,2,4),1,0), "Rho_beta2_TF"=ifelse(RhoConfig[2]%in%c(1,2,4),1,0), "AreaAbundanceCurveTF"=0, "CMP_xmax"=200, "CMP_breakpoint"=1, "Method"=switch(Method,"Mesh"=0,"Grid"=1,"Spherical_mesh"=0,"Stream_network"=2), "Include_F"=ifelse(all(F_ct==0),0,F_init) ) Return = NULL if(Version%in%c("VAST_v1_1_0","VAST_v1_0_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_i"=tprime_iz[,1], "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list() ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_i"=tprime_iz[,1], "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list() ) } if(Version%in%c("VAST_v1_4_0","VAST_v1_3_0","VAST_v1_2_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_f_input"=OverdispersionConfig_input[1], "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_i"=tprime_iz[,1], "v_i"=match(v_i,sort(unique(v_i)))-1, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list() ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_f_input"=OverdispersionConfig_input[1], "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_i"=tprime_iz[,1], "v_i"=match(v_i,sort(unique(v_i)))-1, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list() ) } if(Version%in%c("VAST_v1_6_0","VAST_v1_5_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_f_input"=OverdispersionConfig_input[1], "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_i"=tprime_iz[,1], "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list() ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_f_input"=OverdispersionConfig_input[1], "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_i"=tprime_iz[,1], "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list() ) } if(Version%in%c("VAST_v1_7_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_i"=tprime_iz[,1], "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list() ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_i"=tprime_iz[,1], "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list() ) } if(Version%in%c("VAST_v1_8_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_i"=tprime_iz[,1], "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_i"=tprime_iz[,1], "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v1_9_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_i"=tprime_iz[,1], "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_i"=tprime_iz[,1], "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v2_8_0","VAST_v2_7_0","VAST_v2_6_0","VAST_v2_5_0","VAST_v2_4_0","VAST_v2_3_0","VAST_v2_2_0","VAST_v2_1_0","VAST_v2_0_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel"=ObsModel_ez[1,], "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v3_0_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_i"=c_iz[,1], "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v4_0_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_j"=n_j, "n_p"=n_p, "n_k"=n_k, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v4_4_0","VAST_v4_3_0","VAST_v4_2_0","VAST_v4_1_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "include_data"=TRUE, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "include_data"=TRUE, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v5_0_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "include_data"=TRUE, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "include_data"=TRUE, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v5_1_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "include_data"=TRUE, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "include_data"=TRUE, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v5_2_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "include_data"=TRUE, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "include_data"=TRUE, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v5_4_0","VAST_v5_3_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "include_data"=TRUE, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_vec"=Options_vec, "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "include_data"=TRUE, "Options"=Options2use, "yearbounds_zz"=yearbounds_zz, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v5_5_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xj"=X_xj, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v6_0_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "Xconfig_zcp"=Xconfig_zcp, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=as.vector(FieldConfig_input[1:2,]), "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "Xconfig_zcp"=Xconfig_zcp, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v7_0_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=FieldConfig_input, "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "Xconfig_zcp"=Xconfig_zcp, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_xl, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=FieldConfig_input, "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "Xconfig_zcp"=Xconfig_zcp, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) + } + if(Version%in%c("VAST_v8_0_0")){ + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_g"=n_g, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=FieldConfig_input, "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "Xconfig_zcp"=Xconfig_zcp, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_gl"=a_gl, "X_itp"=X_itp, "X_gtp"=X_gtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_gm"=Z_gm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2, "Ais_ij"=cbind(spatial_list$A_is@i,spatial_list$A_is@j), "Ais_x"=spatial_list$A_is@x, "Ags_ij"=cbind(spatial_list$A_gs@i,spatial_list$A_gs@j), "Ags_x"=spatial_list$A_gs@x ) } if( is.null(Return) ) stop("`Version` provided does not match the list of possible values") if( "spde" %in% names(Return) ) Return[['spde']] = MeshList$isotropic_spde$param.inla[c("M0","M1","M2")] diff --git a/inst/executables/VAST_v8_0_0.cpp b/inst/executables/VAST_v8_0_0.cpp new file mode 100644 index 0000000..bd599be --- /dev/null +++ b/inst/executables/VAST_v8_0_0.cpp @@ -0,0 +1,2013 @@ +#include +#include + +// Function to import R list for user-defined Options_vec and Options, packaged as list Options_list in TmbData +template +struct options_list { + vector Options_vec; + vector Options; + matrix yearbounds_zz; + matrix Expansion_cz; + options_list(SEXP x){ // Constructor + Options_vec = asVector(getListElement(x,"Options_vec")); + Options = asVector(getListElement(x,"Options")); + yearbounds_zz = asMatrix(getListElement(x,"yearbounds_zz")); + Expansion_cz = asMatrix(getListElement(x,"Expansion_cz")); + } +}; + +// Needed for returning SparseMatrix +template +Eigen::SparseMatrix Q_network( Type log_theta, int n_s, vector parent_s, vector child_s, vector dist_s ){ + Eigen::SparseMatrix Q( n_s, n_s ); + Type theta = exp( log_theta ); + for(int s=0; s +bool isNA(Type x){ + return R_IsNA(asDouble(x)); +} + +// Posfun +template +Type posfun(Type x, Type lowerlimit, Type &pen){ + pen += CppAD::CondExpLt(x,lowerlimit,Type(0.01)*pow(x-lowerlimit,2),Type(0)); + return CppAD::CondExpGe(x,lowerlimit,x,lowerlimit/(Type(2)-x/lowerlimit)); +} + +// Variance +template +Type var( array vec ){ + Type vec_mod = vec - (vec.sum()/vec.size()); + Type res = pow(vec_mod, 2).sum() / vec.size(); + return res; +} + +// dlnorm +template +Type dlnorm(Type x, Type meanlog, Type sdlog, int give_log=0){ + //return 1/(sqrt(2*M_PI)*sd)*exp(-.5*pow((x-mean)/sd,2)); + Type logres = dnorm( log(x), meanlog, sdlog, true) - log(x); + if(give_log) return logres; else return exp(logres); +} + +// Generate loadings matrix +template +matrix loadings_matrix( vector L_val, int n_rows, int n_cols ){ + matrix L_rc(n_rows, n_cols); + int Count = 0; + for(int r=0; r=c){ + L_rc(r,c) = L_val(Count); + Count++; + }else{ + L_rc(r,c) = 0.0; + } + }} + return L_rc; +} + +// IN: eta1_vf; L1_z +// OUT: jnll_comp; eta1_vc +// eta_jf could be either eta_vf (for overdispersion) or eta_tf (for year effects) +template +matrix covariation_by_category_nll( int n_f, int n_j, int n_c, matrix eta_jf, matrix eta_mean_jf, vector L_z, Type &jnll_pointer, objective_function* of){ + using namespace density; + matrix eta_jc(n_j, n_c); + vector Tmp_c; + // IID + if( n_f == -2 ){ + for( int j=0; j::value && of->do_simulate) { + eta_jf(j,f) = rnorm( eta_mean_jf(j,f), Type(1.0) ); + } + // Rescale + eta_jc(j,c) = eta_jf(j,f) * L_z(f); + }} + } + // Turn off + if( n_f == -1 ){ + eta_jc.setZero(); + } + // AR1 structure + if( n_f==0 ){ + for( int j=0; j::value && of->do_simulate){ + SCALE( AR1(L_z(1)), exp(L_z(0)) ).simulate(Tmp_c); + eta_jf.row(j) = Tmp_c; + } + } + eta_jc = eta_jf; + } + // Factor analysis structure + if( n_f>0 ){ + // Assemble the loadings matrix + matrix L_cf = loadings_matrix( L_z, n_c, n_f ); + // Probability of overdispersion + for( int j=0; j::value && of->do_simulate){ + eta_jf(j,f) = rnorm( eta_mean_jf(j,f), Type(1.0) ); + } + }} + // Multiply out overdispersion + eta_jc = eta_jf * L_cf.transpose(); + } + return eta_jc; +} + +template // +matrix convert_upper_cov_to_cor( matrix cov ){ + int nrow = cov.row(0).size(); + for( int i=0; i // +matrix gmrf_by_category_nll( int n_f, int method, int timing, int n_s, int n_c, Type logkappa, array gmrf_input_sf, array gmrf_mean_sf, vector L_z, density::GMRF_t gmrf_Q, Type &jnll_pointer, objective_function* of){ + using namespace density; + matrix gmrf_sc(n_s, n_c); + vector gmrf_s(n_s); + matrix Cov_cc(n_c,n_c); + array diff_gmrf_sc(n_s, n_c); // Requires an array + Type logtau; + if(method==0) logtau = log( 1 / (exp(logkappa) * sqrt(4*M_PI)) ); + if(method==1) logtau = log( 1 / sqrt(1-exp(logkappa*2)) ); + if( (method!=0) & (method!=1) ) logtau = Type(0.0); + // IID + if(n_f == -2){ + for( int c=0; c::value && of->do_simulate) { + gmrf_Q.simulate(gmrf_s); + gmrf_input_sf.col(f) = gmrf_s + gmrf_mean_sf.col(f); + } + // Rescale + gmrf_sc.col(c) = gmrf_input_sf.col(f) / exp(logtau) * L_z(f); // Rescaling from comp_index_v1d.cpp + } + } + // Turn off + if(n_f == -1){ + gmrf_sc.setZero(); + } + // AR1 structure + if(n_f==0){ + jnll_pointer += SEPARABLE( AR1(L_z(1)), gmrf_Q )(gmrf_input_sf - gmrf_mean_sf); + // Simulate new values when using obj.simulate() + if(isDouble::value && of->do_simulate) { + SEPARABLE( AR1(L_z(1)), gmrf_Q ).simulate(gmrf_input_sf); + gmrf_input_sf += gmrf_input_sf; + } + // Rescale + logtau = L_z(0) - logkappa; // + gmrf_sc = gmrf_input_sf / exp(logtau); // Rescaling from comp_index_v1d.cpp + } + // Factor analysis structure + if(n_f>0){ + // PDF if density-dependence/interactions occurs prior to correlated dynamics + if( timing==0 ){ + for( int f=0; f::value && of->do_simulate) { + gmrf_Q.simulate(gmrf_s); + gmrf_input_sf.col(f) = gmrf_s + gmrf_mean_sf.col(f); + } + } + // Rescale + matrix L_cf = loadings_matrix( L_z, n_c, n_f ); + gmrf_sc = (gmrf_input_sf.matrix() * L_cf.transpose()) / exp(logtau); + } + // PDF if density-dependence/interactions occurs after correlated dynamics (Only makes sense if n_f == n_c) + if( timing==1 ){ + // Calculate difference without rescaling + gmrf_sc = gmrf_input_sf.matrix(); + for( int s=0; s L_cf = loadings_matrix( L_z, n_c, n_f ); + Cov_cc = L_cf * L_cf.transpose(); + jnll_pointer += SCALE(SEPARABLE(MVNORM(Cov_cc), gmrf_Q), exp(-logtau))( diff_gmrf_sc ); + //gmrf_sc = gmrf_sc / exp(logtau); + // Simulate new values when using obj.simulate() + if(isDouble::value && of->do_simulate) { + SEPARABLE(MVNORM(Cov_cc), gmrf_Q).simulate( diff_gmrf_sc ); + gmrf_sc = gmrf_mean_sf + diff_gmrf_sc/exp(logtau); + } + } + } + return gmrf_sc; +} + +// Used to calculate GMRF PDF for initial condition given covariance Cov_cc +// Only makes sense given: +// 1. full-rank factor model +// 2. Spatial Gompertz model conditions +// 3. Timing = 1 +template +matrix gmrf_stationary_nll( int method, int n_s, int n_c, Type logkappa, array gmrf_input_sc, matrix Cov_cc, density::GMRF_t gmrf_Q, Type &jnll_pointer, objective_function* of){ + using namespace density; + array gmrf_sc(n_s, n_c); + Type logtau; + if(method==0) logtau = log( 1 / (exp(logkappa) * sqrt(4*M_PI)) ); + if(method==1) logtau = log( 1 / sqrt(1-exp(logkappa*2)) ); + if( (method!=0) & (method!=1) ) logtau = Type(0.0); + // PDF if density-dependence/interactions occurs after correlated dynamics (Only makes sense if n_f == n_c) + gmrf_sc = gmrf_input_sc.matrix(); + // Calculate likelihood + jnll_pointer += SCALE(SEPARABLE(MVNORM(Cov_cc), gmrf_Q), exp(-logtau))( gmrf_sc ); + // Simulate new values when using obj.simulate() + if(isDouble::value && of->do_simulate) { + SEPARABLE(MVNORM(Cov_cc), gmrf_Q).simulate( gmrf_sc ); + gmrf_sc = gmrf_sc / exp(logtau); + } + return gmrf_sc.matrix(); +} + +// CMP distribution +template +Type dCMP(Type x, Type mu, Type nu, int give_log=0, int iter_max=30, int break_point=10){ + // Explicit + Type ln_S_1 = nu*mu - ((nu-1)/2)*log(mu) - ((nu-1)/2)*log(2*M_PI) - 0.5*log(nu); + // Recursive + vector S_i(iter_max); + S_i(0) = 1; + for(int i=1; i +Type dPoisGam( Type x, Type shape, Type scale, Type intensity, vector &diag_z, int maxsum=50, int minsum=1, int give_log=0 ){ + // Maximum integration constant to prevent numerical overflow, but capped at value for maxsum to prevent numerical underflow when subtracting by a higher limit than is seen in the sequence + Type max_log_wJ, z1, maxJ_bounded; + if( x==0 ){ + diag_z(0) = 1; + max_log_wJ = 0; + diag_z(1) = 0; + }else{ + z1 = log(intensity) + shape*log(x/scale) - shape*log(shape) + 1; + diag_z(0) = exp( (z1 - 1) / (1 + shape) ); + maxJ_bounded = CppAD::CondExpGe(diag_z(0), Type(maxsum), Type(maxsum), diag_z(0)); + max_log_wJ = maxJ_bounded*log(intensity) + (maxJ_bounded*shape)*log(x/scale) - lgamma(maxJ_bounded+1) - lgamma(maxJ_bounded*shape); + diag_z(1) = diag_z(0)*log(intensity) + (diag_z(0)*shape)*log(x/scale) - lgamma(diag_z(0)+1) - lgamma(diag_z(0)*shape); + } + // Integration constant + Type W = 0; + Type log_w_j; + //Type pos_penalty; + for( int j=minsum; j<=maxsum; j++ ){ + Type j2 = j; + //W += pow(intensity,j) * pow(x/scale, j2*shape) / exp(lgamma(j2+1)) / exp(lgamma(j2*shape)) / exp(max_log_w_j); + log_w_j = j2*log(intensity) + (j2*shape)*log(x/scale) - lgamma(j2+1) - lgamma(j2*shape); + //W += exp( posfun(log_w_j, Type(-30), pos_penalty) ); + W += exp( log_w_j - max_log_wJ ); + if(j==minsum) diag_z(2) = log_w_j; + if(j==maxsum) diag_z(3) = log_w_j; + } + // Loglikelihood calculation + Type loglike = 0; + if( x==0 ){ + loglike = -intensity; + }else{ + loglike = -x/scale - intensity - log(x) + log(W) + max_log_wJ; + } + // Return + if(give_log) return loglike; else return exp(loglike); +} + +// Calculate B_cc +template +matrix calculate_B( int method, int n_f, int n_r, matrix Chi_fr, matrix Psi_fr, Type &jnll_pointer ){ + matrix B_ff( n_f, n_f ); + matrix BplusI_ff( n_f, n_f ); + matrix Chi_rf = Chi_fr.transpose(); + matrix Psi_rf = Psi_fr.transpose(); + matrix Identity_ff( n_f, n_f ); + Identity_ff.setIdentity(); + + // No interactions (default) + if( method==0 ){ + B_ff.setZero(); + } + // Simple co-integration -- complex unbounded eigenvalues + if( method==1 ){ + B_ff = Chi_fr * Psi_rf; + } + // Real eigenvalues + if( method==2 ){ + matrix Chi_ff( n_f, n_f ); + Chi_ff = Identity_ff; + // Make Chi_ff + vector colnorm_r( n_r ); + colnorm_r.setZero(); + for(int f=0; f Psi_ff( n_f, n_f ); + Psi_ff = Identity_ff; + for(int f=n_r; f L_ff(n_f, n_f); + L_ff.setZero(); + for(int r=0; r invChi_ff = atomic::matinv( Chi_ff ); + matrix trans_Psi_ff = Psi_ff.transpose(); + matrix trans_invPsi_ff = atomic::matinv( Psi_ff ).transpose(); + B_ff = Chi_ff * trans_Psi_ff; + B_ff = B_ff * L_ff; + B_ff = B_ff * trans_invPsi_ff; + B_ff = B_ff * invChi_ff; + // Penalize colnorm_r + jnll_pointer += ( log(colnorm_r)*log(colnorm_r) ).sum(); + } + // Complex bounded eigenvalues + if( method==3 ){ + BplusI_ff = Chi_fr * Psi_rf + Identity_ff; + // Extract eigenvalues + vector< std::complex > eigenvalues_B_ff = B_ff.eigenvalues(); + vector real_eigenvalues_B_ff = eigenvalues_B_ff.real(); + vector imag_eigenvalues_B_ff = eigenvalues_B_ff.imag(); + vector mod_eigenvalues_B_ff( n_f ); + // Calculate maximum eigenvalues + Type MaxEigen = 1; + for(int f=0; f +matrix stationary_variance( int n_c, matrix B_cc, matrix Cov_cc ){ + int n2_c = n_c*n_c; + matrix Kronecker_c2c2(n2_c,n2_c); + matrix InvDiff_c2c2(n2_c, n2_c); + matrix Vinf_cc(n_c, n_c); + Kronecker_c2c2 = kronecker( B_cc, B_cc ); + InvDiff_c2c2.setIdentity(); + InvDiff_c2c2 = InvDiff_c2c2 - Kronecker_c2c2; + InvDiff_c2c2 = atomic::matinv( InvDiff_c2c2 ); + Vinf_cc.setZero(); + for(int i=0; i +Type objective_function::operator() () +{ + using namespace R_inla; + using namespace Eigen; + using namespace density; + + // Dimensions + DATA_INTEGER(n_i); // Number of observations (stacked across all years) + DATA_INTEGER(n_s); // Number of "strata" (i.e., vectices in SPDE mesh) + DATA_INTEGER(n_g); // Number of extrapolation-grid cells + DATA_INTEGER(n_t); // Number of time-indices + DATA_INTEGER(n_c); // Number of categories (e.g., length bins) + DATA_INTEGER(n_e); // Number of error distributions + DATA_INTEGER(n_p); // Number of dynamic covariates + DATA_INTEGER(n_v); // Number of tows/vessels (i.e., levels for the factor explaining overdispersion) + DATA_INTEGER(n_l); // Number of indices to post-process + DATA_INTEGER(n_m); // Number of range metrics to use (probably 2 for Eastings-Northings) + + // Config + DATA_STRUCT( Options_list, options_list ); + // Options_list.Options_vec + // Slot 0 -- Aniso: 0=No, 1=Yes + // Slot 1 -- DEPRECATED + // Slot 2 -- AR1 on beta1 (year intercepts for 1st linear predictor) to deal with missing years: 0=No, 1=Yes + // Slot 3 -- AR1 on beta2 (year intercepts for 2nd linear predictor) to deal with missing years: 0=No, 1=Yes + // Slot 4 -- DEPRECATED + // Slot 5 -- Upper limit constant of integration calculation for infinite-series density functions (Conway-Maxwell-Poisson and Tweedie) + // Slot 6 -- Breakpoint in CMP density function + // Slot 7 -- Whether to use SPDE or 2D-AR1 hyper-distribution for spatial process: 0=SPDE; 1=2D-AR1; 2=Stream-network + // Slot 8 -- Whether to use F_ct or ignore it for speedup + // Options_list.Options + // Slot 0: Calculate SE for Index_xctl + // Slot 1: Calculate SE for log(Index_xctl) + // Slot 2: Calculate mean_Z_ctm (i.e., center-of-gravity) + // Slot 3: Calculate SE for D_i (expected density for every observation) + // Slot 4: Calculate mean_D_tl and effective_area_tl + // Slot 5: Calculate standard errors for Covariance and Correlation among categories using factor-analysis parameterization + // Slot 6: Calculate synchrony for different periods specified via yearbounds_zz + // Slot 7: Calculate coherence and variance for Epsilon1_sct and Epsilon2_sct + // Slot 8: Calculate proportions and SE + // Slot 9: Include normalization in GMRF PDF + // Slot 10: Calculate Fratio as F_ct divided by F achieving 40% of B0 + // Slot 11: Calculate B0 and Bratio + // Options_list.yearbounds_zz + // Two columns, and 1+ rows, specifying first and last t for each period used in calculating synchrony + // Options_list.Expansion_cz + // Two columns and n_c rows. 1st column: Type of expansion (0=area-expansion; 1=biomass-expansion); 2nd column: Category used for biomass-expansion + DATA_IMATRIX(FieldConfig); // Input settings (vector, length 4) + DATA_IVECTOR(RhoConfig); + DATA_IVECTOR(OverdispersionConfig); // Input settings (vector, length 2) + DATA_IMATRIX(ObsModel_ez); // Observation model + // Column 0: Probability distribution for data for each level of e_i + // Column 1: Link function for linear predictors for each level of c_i + // NOTE: nlevels(c_i) must be <= nlevels(e_i) + DATA_IVECTOR(VamConfig); + // Slot 0 -- method for calculating n_c-by-n_c interaction matrix, B_ff + // Slot 1 -- rank of interaction matrix B_ff + // Current implementation only makes sense when (1) intercepts are constant among years; (2) using a Poisson-link delta model; (3) n_f=n_c for spatio-temporal variation; (4) starts near equilibrium manifold + DATA_IARRAY(Xconfig_zcp); + // Row 0 -- Methods for 1st component for each covariate in X_xtp (0=Off; 1=Estimate; 2=Estimate with spatially varying coefficient) + // Row 1 -- Methods for 2nd component for each covariate in X_xtp (0=Off; 1=Estimate; 2=Estimate with spatially varying coefficient) + DATA_INTEGER(include_data); // Always use TRUE except for internal usage to extract GRMF normalization when turn off GMRF normalization in CPP + + // Data vectors + DATA_VECTOR(b_i); // Response (biomass) for each observation + DATA_VECTOR(a_i); // Area swept for each observation (km^2) + DATA_IMATRIX(c_iz); // Category for each observation + DATA_IVECTOR(e_i); // Error distribution for each observation + DATA_IMATRIX(t_iz); // Time-indices (year, season, etc.) for each observation + DATA_IVECTOR(v_i); // tows/vessels for each observation (level of factor representing overdispersion) + DATA_VECTOR(PredTF_i); // vector indicating whether an observatino is predictive (1=used for model evaluation) or fitted (0=used for parameter estimation) + DATA_MATRIX(a_gl); // Area for each "real" stratum(km^2) in each stratum + DATA_ARRAY(X_itp); // Covariate design matrix (strata x covariate) + DATA_ARRAY(X_gtp); // Covariate design matrix (strata x covariate) + DATA_MATRIX(Q_ik); // Catchability matrix (observations x variable) + DATA_IMATRIX(t_yz); // Matrix for time-indices of calculating outputs (abundance index and "derived-quantity") + DATA_MATRIX(Z_gm); // Derived quantity matrix + DATA_MATRIX(F_ct); // Matrix of annual fishing mortality for each category + + // Spatial network inputs + DATA_IVECTOR(parent_s); // Columns: 0=Parent index, 1=Child index, 2=Distance from parent to child + DATA_IVECTOR(child_s); // Columns: 0=Parent index, 1=Child index, 2=Distance from parent to child + DATA_VECTOR(dist_s); // Columns: 0=Parent index, 1=Child index, 2=Distance from parent to child + + // SPDE objects + DATA_STRUCT(spde,spde_t); + + // Aniso objects + DATA_STRUCT(spde_aniso,spde_aniso_t); + + // Sparse matrices for precision matrix of 2D AR1 process + // Q = M0*(1+rho^2)^2 + M1*(1+rho^2)*(-rho) + M2*rho^2 + DATA_SPARSE_MATRIX(M0); + DATA_SPARSE_MATRIX(M1); + DATA_SPARSE_MATRIX(M2); + + // Projection matrices from knots s to data i or extrapolation-grid cells x + //DATA_SPARSE_MATRIX(A_is); + //DATA_SPARSE_MATRIX(A_gs); + DATA_IMATRIX( Ais_ij ); + DATA_VECTOR( Ais_x ); + DATA_IMATRIX( Ags_ij ); + DATA_VECTOR( Ags_x ); + + // Parameters + PARAMETER_VECTOR(ln_H_input); // Anisotropy parameters + PARAMETER_MATRIX(Chi_fr); // error correction responses + PARAMETER_MATRIX(Psi_fr); // error correction loadings, B_ff = Chi_fr %*% t(Psi_fr) + + // -- presence/absence fixed effects + PARAMETER_MATRIX(beta1_ft); // Year effect + PARAMETER_ARRAY(gamma1_ctp); // Dynamic covariate effect + PARAMETER_VECTOR(lambda1_k); // Catchability coefficients + PARAMETER_VECTOR(L1_z); // Overdispersion parameters + PARAMETER_VECTOR(L_omega1_z); + PARAMETER_VECTOR(L_epsilon1_z); + PARAMETER_VECTOR(L_beta1_z); + PARAMETER(logkappa1); + PARAMETER_VECTOR(Beta_mean1_c); // mean-reversion for beta1_ft + PARAMETER_VECTOR(Beta_rho1_f); // AR1 for presence/absence Beta component, Default=0 + PARAMETER_VECTOR(Epsilon_rho1_f); // AR1 for presence/absence Epsilon component, Default=0 + PARAMETER_ARRAY(log_sigmaXi1_cp); // log-SD of Xi1_scp + PARAMETER_VECTOR(log_sigmaratio1_z); // Ratio of variance for columns of t_iz + + // -- presence/absence random effects + PARAMETER_MATRIX(eta1_vf); + PARAMETER_ARRAY(Xiinput1_scp); // spatially varying coefficient + PARAMETER_ARRAY(Omegainput1_sf); // Expectation + PARAMETER_ARRAY(Epsiloninput1_sft); // Annual variation + + // -- positive catch rates fixed effects + PARAMETER_MATRIX(beta2_ft); // Year effect + PARAMETER_ARRAY(gamma2_ctp); // Dynamic covariate effect + PARAMETER_VECTOR(lambda2_k); // Catchability coefficients + PARAMETER_VECTOR(L2_z); // Overdispersion parameters + PARAMETER_VECTOR(L_omega2_z); + PARAMETER_VECTOR(L_epsilon2_z); + PARAMETER_VECTOR(L_beta2_z); + PARAMETER(logkappa2); + PARAMETER_VECTOR(Beta_mean2_c); // mean-reversion for beta2_t + PARAMETER_VECTOR(Beta_rho2_f); // AR1 for positive catch Beta component, Default=0 + PARAMETER_VECTOR(Epsilon_rho2_f); // AR1 for positive catch Epsilon component, Default=0 + PARAMETER_ARRAY(log_sigmaXi2_cp); // log-SD of Xi2_scp + PARAMETER_VECTOR(log_sigmaratio2_z); // Ratio of variance for columns of t_iz + + // Error distribution parameters + PARAMETER_ARRAY(logSigmaM); + // Columns: 0=CV, 1=[usually not used], 2=[usually not used] + // Rows: Each level of e_i and/or c_i + // SigmaM[,0] indexed by e_i, e.g., SigmaM(e_i(i),0) + // SigmaM[,1] and SigmaM[,2] indexed by c_i, e.g., SigmaM(c_i(i),2) + + // -- positive catch rates random effects + PARAMETER_VECTOR(delta_i); + PARAMETER_MATRIX(eta2_vf); + PARAMETER_ARRAY(Xiinput2_scp); // spatially varying coefficient + PARAMETER_ARRAY(Omegainput2_sf); // Expectation + PARAMETER_ARRAY(Epsiloninput2_sft); // Annual variation + + //////////////////////// + // Preparatory bookkeeping + //////////////////////// + + // Indices -- i=Observation; t=Year; c=Category; p=Dynamic-covariate + int i,t,c,p,s,g; + + // Objective function + vector jnll_comp(16); + // Slot 0 -- spatial, encounter + // Slot 1 -- spatio-temporal, encounter + // Slot 2 -- spatial, positive catch + // Slot 3 -- spatio-temporal, positive catch + // Slot 4 -- tow/vessel overdispersion, encounter + // Slot 5 -- tow/vessel overdispersion, positive catch + // Slot 8 -- penalty on beta, encounter + // Slot 9 -- penalty on beta, positive catch + // Slot 10 -- likelihood of data, encounter + // Slot 11 -- likelihood of data, positive catch + // Slot 12 -- Likelihood of Lognormal-Poisson overdispersion delta_i + // Slot 13 -- penalty on estimate_B structure + // Slot 14 -- Spatially varying coefficient, encounter + // Slot 15 -- Spatially varying coefficient, positive catch + jnll_comp.setZero(); + Type jnll = 0; + + // Unpack Options_list + vector Options_vec( Options_list.Options_vec.size() ); + Options_vec = Options_list.Options_vec; + vector Options( Options_list.Options.size() ); + Options = Options_list.Options; + matrix yearbounds_zz( Options_list.yearbounds_zz.col(0).size(), 2 ); + yearbounds_zz = Options_list.yearbounds_zz; + matrix Expansion_cz( n_c, 2 ); + Expansion_cz = Options_list.Expansion_cz; + + // Derived parameters + Type Range_raw1, Range_raw2; + if( Options_vec(7)==0 ){ + Range_raw1 = sqrt(8) / exp( logkappa1 ); // Range = approx. distance @ 10% correlation + Range_raw2 = sqrt(8) / exp( logkappa2 ); // Range = approx. distance @ 10% correlation + } + if( (Options_vec(7)==1) | (Options_vec(7)==2) ){ + Range_raw1 = log(0.1) / logkappa1; // Range = approx. distance @ 10% correlation + Range_raw2 = log(0.1) / logkappa2; // Range = approx. distance @ 10% correlation + } + array SigmaM( n_e, 3 ); + array sigmaXi1_cp( n_c, n_p ); + array sigmaXi2_cp( n_c, n_p ); + SigmaM = exp( logSigmaM ); + sigmaXi1_cp = exp( log_sigmaXi1_cp ); + sigmaXi2_cp = exp( log_sigmaXi2_cp ); + + // Anisotropy elements + matrix H(2,2); + H(0,0) = exp(ln_H_input(0)); + H(1,0) = ln_H_input(1); + H(0,1) = ln_H_input(1); + H(1,1) = (1+ln_H_input(1)*ln_H_input(1)) / exp(ln_H_input(0)); + + // Overwrite parameters when mirroring them + if( RhoConfig(1)==6 ){ + Beta_rho2_f = Beta_rho1_f; + } + if( RhoConfig(3)==6 ){ + Epsilon_rho2_f = Epsilon_rho1_f; + } + + //////////////////////// + // Interactions and fishing mortality + //////////////////////// + + // Define interaction matrix for Epsilon1, and also the impact of F_ct on intercepts + int n_f1; + n_f1 = Epsiloninput1_sft.col(0).cols(); + int n_f2; + n_f2 = Epsiloninput2_sft.col(0).cols(); + matrix B_ff( n_f1, n_f1 ); // Interactions among factors + B_ff = calculate_B( VamConfig(0), n_f1, VamConfig(1), Chi_fr, Psi_fr, jnll_comp(13) ); + matrix iota_ct( n_c, n_t ); // Cumulative impact of fishing mortality F_ct in years <= current year t + matrix B1_cc( n_c, n_c ); // Interactions among categories + matrix covE1_cc( n_c, n_c ); + matrix B2_cc( n_c, n_c ); // Interactions among categories + matrix covE2_cc( n_c, n_c ); + matrix I_cc( n_c, n_c ); + matrix IminusB_cc( n_c, n_c ); + I_cc.setIdentity(); + B1_cc.setZero(); + B2_cc.setZero(); + covE1_cc.setZero(); + covE2_cc.setZero(); + // Calculate interaction matrix B_cc for categories if feasible + if( (n_c==n_f1) & (n_c==n_f2) & (FieldConfig(1,0)>0) & (FieldConfig(1,1)>0) ){ + matrix L_epsilon1_cf = loadings_matrix( L_epsilon1_z, n_c, n_f1 ); + matrix Cov_epsilon1_cc = L_epsilon1_cf * L_epsilon1_cf.transpose(); + matrix L_epsilon2_cf = loadings_matrix( L_epsilon2_z, n_c, n_f2 ); + matrix Cov_epsilon2_cc = L_epsilon2_cf * L_epsilon2_cf.transpose(); + matrix Btemp_cc( n_c, n_c ); + // Assemble interaction matrix + B1_cc = B_ff; + for( c=0; c Btarg_c( n_c ); + vector Ftarg_c( n_c ); + matrix Fratio_ct( n_c, n_t ); + IminusB_cc = I_cc - B1_cc; + Btarg_c = log( 0.4 ); // 40% target, transformed for log-link + Ftarg_c = -1 * ( IminusB_cc * Btarg_c ); + for( t=0; t sumB1_cc( n_c, n_c ); + IminusB_cc = I_cc - B1_cc; + sumB1_cc = IminusB_cc.inverse(); + iota_ct.col(0) -= sumB1_cc * F_ct.col(0); + } + if( (Options_vec(8)==1) | (Options_vec(8)==2) ){ + // Project forward effect of F_ct from initial year through current year + for( t=1; t Q1( n_s, n_s ); + Eigen::SparseMatrix Q2( n_s, n_s ); + GMRF_t gmrf_Q; + if( (Options_vec(7)==0) & (Options_vec(0)==0) ){ + Q1 = Q_spde(spde, exp(logkappa1)); + Q2 = Q_spde(spde, exp(logkappa2)); + } + if( (Options_vec(7)==0) & (Options_vec(0)==1) ){ + Q1 = Q_spde(spde_aniso, exp(logkappa1), H); + Q2 = Q_spde(spde_aniso, exp(logkappa2), H); + } + if( Options_vec(7)==1 ){ + Q1 = M0*pow(1+exp(logkappa1*2),2) + M1*(1+exp(logkappa1*2))*(-exp(logkappa1)) + M2*exp(logkappa1*2); + Q2 = M0*pow(1+exp(logkappa2*2),2) + M1*(1+exp(logkappa2*2))*(-exp(logkappa2)) + M2*exp(logkappa2*2); + } + if( Options_vec(7)==2 ){ + Q1 = Q_network( logkappa1, n_s, parent_s, child_s, dist_s ); + Q2 = Q_network( logkappa2, n_s, parent_s, child_s, dist_s ); + } + + ///// + // 1st component + ///// + gmrf_Q = GMRF( Q1, bool(Options(9)) ); + + // Omega1 + array Omegamean1_sf(n_s, Omegainput1_sf.cols() ); + Omegamean1_sf.setZero(); + array Omega1_sc(n_s, n_c); + Omega1_sc = gmrf_by_category_nll(FieldConfig(0,0), Options_vec(7), VamConfig(2), n_s, n_c, logkappa1, Omegainput1_sf, Omegamean1_sf, L_omega1_z, gmrf_Q, jnll_comp(0), this); + + // Projection for Omega1 + array Omega1_iz(n_i, c_iz.cols()); + array Omega1_gc(n_g, n_c); + Omega1_iz.setZero(); + Omega1_gc.setZero(); + for( int Arow=0; Arow=0) & (c_iz(i,zc)=0) & (c_iz(i,zc) Epsilonmean1_sf(n_s, n_f1 ); + // PDF for Epsilon1 + array Epsilon1_sct(n_s, n_c, n_t); + for(t=0; t=(Options(11)+1) ){ + // Prediction for spatio-temporal component + // Default, and also necessary whenever VamConfig(2)==1 & n_f1!=n_c + if( (VamConfig(0)==0) | ((n_f1!=n_c) & (VamConfig(2)==1)) ){ + // If no interactions, then just autoregressive for factors + for(s=0; s Epsilon1_izz(n_i, c_iz.cols(), t_iz.cols()); + array Epsilon1_gct(n_g, n_c, n_t); + Epsilon1_izz.setZero(); + Epsilon1_gct.setZero(); + for( int Arow=0; Arow=0) & (c_iz(i,zc)=0) & (t_iz(i,zt)=0) & (c_iz(i,zc)=0) & (t_iz(i,zt) Ximean1_sc(n_s, 1); + array Xi1_scp(n_s, n_c, n_p); + vector Sigma1(1); + array Tmp1_sc(n_s, 1); + Ximean1_sc.setZero(); + Xi1_scp.setZero(); + for(p=0; p Xi1_izp(n_i, c_iz.cols(), n_p); + array Xi1_gcp(n_g, n_c, n_p); + Xi1_izp.setZero(); + Xi1_gcp.setZero(); + for( int Arow=0; Arow=0) & (c_iz(i,zc)=0) & (c_iz(i,zc) Omegamean2_sf(n_s, Omegainput2_sf.cols() ); + Omegamean2_sf.setZero(); + array Omega2_sc(n_s, n_c); + Omega2_sc = gmrf_by_category_nll(FieldConfig(0,1), Options_vec(7), VamConfig(2), n_s, n_c, logkappa2, Omegainput2_sf, Omegamean2_sf, L_omega2_z, gmrf_Q, jnll_comp(2), this); + + // Projection for Omega2 + array Omega2_iz(n_i, c_iz.cols()); + array Omega2_gc(n_g, n_c); + Omega2_iz.setZero(); + Omega2_gc.setZero(); + for( int Arow=0; Arow=0) & (c_iz(i,zc)=0) & (c_iz(i,zc) Epsilonmean2_sf(n_s, n_f2); + // PDF for Epsilon2 + array Epsilon2_sct(n_s, n_c, n_t); + for(t=0; t=(Options(11)+1) ){ + // Prediction for spatio-temporal component + // Default, and also necessary whenever VamConfig(2)==1 & n_f2!=n_c + if( (VamConfig(0)==0) | ((n_f2!=n_c) & (VamConfig(2)==1)) ){ + // If no interactions, then just autoregressive for factors + for(s=0; s Epsilon2_izz(n_i, c_iz.cols(), t_iz.cols()); + array Epsilon2_gct(n_g, n_c, n_t); + Epsilon2_izz.setZero(); + Epsilon2_gct.setZero(); + for( int Arow=0; Arow=0) & (c_iz(i,zc)=0) & (t_iz(i,zt)=0) & (c_iz(i,zc)=0) & (t_iz(i,zt) Ximean2_sc(n_s, 1); + array Xi2_scp(n_s, n_c, n_p); + vector Sigma2(1); + array Tmp2_sc(n_s, 1); + Ximean2_sc.setZero(); + Xi2_scp.setZero(); + for(p=0; p Xi2_izp(n_i, c_iz.cols(), n_p); + array Xi2_gcp(n_g, n_c, n_p); + Xi2_izp.setZero(); + Xi2_gcp.setZero(); + for( int Arow=0; Arow=0) & (c_iz(i,zc)=0) & (c_iz(i,zc) eta1_mean_vf(n_v, n_eta_f1); + eta1_mean_vf.setZero(); + matrix eta1_vc(n_v, n_c); + eta1_vc = covariation_by_category_nll( OverdispersionConfig(0), n_v, n_c, eta1_vf, eta1_mean_vf, L1_z, jnll_comp(4), this ); + // 1st component + int n_eta_f2; + n_eta_f2 = eta2_vf.cols(); + matrix eta2_mean_vf(n_v, n_eta_f2); + eta2_mean_vf.setZero(); + matrix eta2_vc(n_v, n_c); + eta2_vc = covariation_by_category_nll( OverdispersionConfig(1), n_v, n_c, eta2_vf, eta1_mean_vf, L2_z, jnll_comp(5), this ); + + ////// Probability of correlated innovations on intercepts + // 1st component + Type jnll_beta1 = 0; + int n_beta_f1; + n_beta_f1 = beta1_ft.rows(); + matrix beta1_mean_tf(n_t, n_beta_f1); + matrix beta1_tf( n_t, n_beta_f1 ); + beta1_tf = beta1_ft.transpose(); + for( int f=0; f beta1_tc(n_t, n_c); + beta1_tc = covariation_by_category_nll( FieldConfig(2,0), n_t, n_c, beta1_tf, beta1_mean_tf, L_beta1_z, jnll_beta1, this ); + for( c=0; c beta2_mean_tf(n_t, n_beta_f2); + matrix beta2_tf( n_t, n_beta_f2 ); + beta2_tf = beta2_ft.transpose(); + for( int f=0; f beta2_tc(n_t, n_c); + beta2_tc = covariation_by_category_nll( FieldConfig(2,1), n_t, n_c, beta2_tf, beta2_mean_tf, L_beta2_z, jnll_beta2, this ); + for( c=0; c zeta1_i = Q_ik * lambda1_k.matrix(); + vector zeta2_i = Q_ik * lambda2_k.matrix(); + array eta1_izz(n_i, c_iz.cols(), t_iz.cols()); + array eta2_izz(n_i, c_iz.cols(), t_iz.cols()); + array eta1_gct(n_g, n_c, n_t); + array eta2_gct(n_g, n_c, n_t); + eta1_izz.setZero(); + eta1_izz.setZero(); + eta1_gct.setZero(); + eta2_gct.setZero(); + for(p=0; p=0) & (c_iz(i,zc)=0) & (t_iz(i,zt) var_i(n_i); + Type tmp_calc1; + Type tmp_calc2; + // Linear predictor (pre-link) for presence/absence component + matrix P1_iz(n_i,c_iz.row(0).size()); + // Response predictor (post-link) + // ObsModel_ez(e,0) = 0:4 or 11:12: probability ("phi") that data is greater than zero + // ObsModel_ez(e,0) = 5 (ZINB): phi = 1-ZeroInflation_prob -> Pr[D=0] = NB(0|mu,var)*phi + (1-phi) -> Pr[D>0] = phi - NB(0|mu,var)*phi + vector R1_i(n_i); + vector log_one_minus_R1_i(n_i); + vector log_R1_i(n_i); + vector LogProb1_i(n_i); + // Linear predictor (pre-link) for positive component + matrix P2_iz(n_i,c_iz.row(0).size()); + // Response predictor (post-link) + // ObsModel_ez(e,0) = 0:3, 11:12: expected value of data, given that data is greater than zero -> E[D] = mu*phi + // ObsModel_ez(e,0) = 4 (ZANB): expected value ("mu") of neg-bin PRIOR to truncating Pr[D=0] -> E[D] = mu/(1-NB(0|mu,var))*phi ALSO Pr[D] = NB(D|mu,var)/(1-NB(0|mu,var))*phi + // ObsModel_ez(e,0) = 5 (ZINB): expected value of data for non-zero-inflation component -> E[D] = mu*phi + vector R2_i(n_i); + vector LogProb2_i(n_i); + vector maxJ_i(n_i); + vector diag_z(4); + matrix diag_iz(n_i,4); + diag_iz.setZero(); // Used to track diagnostics for Tweedie distribution (columns: 0=maxJ; 1=maxW; 2=lowerW; 3=upperW) + P1_iz.setZero(); + P2_iz.setZero(); + + // Likelihood contribution from observations + LogProb1_i.setZero(); + LogProb2_i.setZero(); + for(i=0; i=0) & (c_iz(i,zc)=0) & (t_iz(i,zt)=0) & (c_iz(i,zc) 0 ){ + LogProb1_i(i) = log_R1_i(i); + }else{ + LogProb1_i(i) = log_one_minus_R1_i(i); + } + }else{ + if( b_i(i) > 0 ){ + LogProb1_i(i) = log( R1_i(i) ); + }else{ + LogProb1_i(i) = log( 1-R1_i(i) ); + } + } + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = rbinom( Type(1), R1_i(i) ); + } + // Positive density likelihood -- models with continuous positive support + if( b_i(i) > 0 ){ // 1e-500 causes overflow on laptop + if(ObsModel_ez(e_i(i),0)==0){ + LogProb2_i(i) = dnorm(b_i(i), R2_i(i), SigmaM(e_i(i),0), true); + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = rnorm( R2_i(i), SigmaM(e_i(i),0) ); + } + } + if(ObsModel_ez(e_i(i),0)==1){ + LogProb2_i(i) = dlnorm(b_i(i), log(R2_i(i))-pow(SigmaM(e_i(i),0),2)/2, SigmaM(e_i(i),0), true); // log-space + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = exp(rnorm( log(R2_i(i))-pow(SigmaM(e_i(i),0),2)/2, SigmaM(e_i(i),0) )); + } + } + if(ObsModel_ez(e_i(i),0)==2){ + LogProb2_i(i) = dgamma(b_i(i), 1/pow(SigmaM(e_i(i),0),2), R2_i(i)*pow(SigmaM(e_i(i),0),2), true); // shape = 1/CV^2, scale = mean*CV^2 + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = rgamma( 1/pow(SigmaM(e_i(i),0),2), R2_i(i)*pow(SigmaM(e_i(i),0),2) ); + } + } + }else{ + LogProb2_i(i) = 0; + } + } + // Likelihood for Tweedie model with continuous positive support + if(ObsModel_ez(e_i(i),0)==8){ + LogProb1_i(i) = 0; + //dPoisGam( Type x, Type shape, Type scale, Type intensity, Type &max_log_w_j, int maxsum=50, int minsum=1, give_log=0 ) + LogProb2_i(i) = dPoisGam( b_i(i), SigmaM(e_i(i),0), R2_i(i), R1_i(i), diag_z, Options_vec(5), Options_vec(6), true ); + diag_iz.row(i) = diag_z; + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = 0; // Option not available + } + } + // Likelihood #2 for Tweedie model with continuous positive support + if(ObsModel_ez(e_i(i),0)==10){ + // Packaged code + LogProb1_i(i) = 0; + // dtweedie( Type y, Type mu, Type phi, Type p, int give_log=0 ) + // R1*R2 = mean + LogProb2_i(i) = dtweedie( b_i(i), R1_i(i)*R2_i(i), R1_i(i), invlogit(SigmaM(e_i(i),0))+1.0, true ); + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = 0; // Option not available + } + } + ///// Likelihood for models with discrete support + // Zero-inflated negative binomial (not numerically stable!) + if(ObsModel_ez(e_i(i),0)==5){ + var_i(i) = R2_i(i)*(1.0+SigmaM(e_i(i),0)) + pow(R2_i(i),2.0)*SigmaM(c_iz(i,0),1); + if( b_i(i)==0 ){ + //LogProb2_i(i) = log( (1-R1_i(i)) + dnbinom2(Type(0.0), R2_i(i), var_i(i), false)*R1_i(i) ); // Pr[X=0] = 1-phi + NB(X=0)*phi + LogProb2_i(i) = logspace_add( log(1-R1_i(i)), dnbinom2(Type(0.0),R2_i(i),var_i(i),true)+log(R1_i(i)) ); // Pr[X=0] = 1-phi + NB(X=0)*phi + }else{ + LogProb2_i(i) = dnbinom2(b_i(i), R2_i(i), var_i(i), true) + log(R1_i(i)); // Pr[X=x] = NB(X=x)*phi + } + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = rbinom( Type(1), R1_i(i) ); + if( b_i(i)>0 ){ + b_i(i) = rnbinom2( R2_i(i), var_i(i) ); + } + } + } + // Conway-Maxwell-Poisson + if(ObsModel_ez(e_i(i),0)==6){ + LogProb2_i(i) = dCMP(b_i(i), R2_i(i), exp(P1_iz(i,0)), true, Options_vec(5)); + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = 0; // Option not available + } + } + // Zero-inflated Poisson + if(ObsModel_ez(e_i(i),0)==7){ + if( b_i(i)==0 ){ + //LogProb2_i(i) = log( (1-R1_i(i)) + dpois(Type(0.0), R2_i(i), false)*R1_i(i) ); // Pr[X=0] = 1-phi + Pois(X=0)*phi + LogProb2_i(i) = logspace_add( log(1-R1_i(i)), dpois(Type(0.0),R2_i(i),true)+log(R1_i(i)) ); // Pr[X=0] = 1-phi + Pois(X=0)*phi + }else{ + LogProb2_i(i) = dpois(b_i(i), R2_i(i), true) + log(R1_i(i)); // Pr[X=x] = Pois(X=x)*phi + } + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = rbinom( Type(1), R1_i(i) ); + if( b_i(i)>0 ){ + b_i(i) = rpois( R2_i(i) ); + } + } + } + // Binned Poisson (for REEF data: 0=none; 1=1; 2=2-10; 3=>11) + /// Doesn't appear stable given spatial or spatio-temporal variation + if(ObsModel_ez(e_i(i),0)==9){ + vector logdBinPois(4); + logdBinPois(0) = logspace_add( log(1-R1_i(i)), dpois(Type(0.0), R2_i(i), true) + log(R1_i(i)) ); // Pr[X=0] = 1-phi + Pois(X=0)*phi + logdBinPois(1) = dpois(Type(1.0), R2_i(i), true) + log(R1_i(i)); // Pr[X | X>0] = Pois(X)*phi + logdBinPois(2) = dpois(Type(2.0), R2_i(i), true) + log(R1_i(i)); // SUM_J( Pr[X|X>0] ) = phi * SUM_J( Pois(J) ) + for(int j=3; j<=10; j++){ + logdBinPois(2) += logspace_add( logdBinPois(2), dpois(Type(j), R2_i(i), true) + log(R1_i(i)) ); + } + logdBinPois(3) = logspace_sub( log(Type(1.0)), logdBinPois(0) ); + logdBinPois(3) = logspace_sub( logdBinPois(3), logdBinPois(1) ); + logdBinPois(3) = logspace_sub( logdBinPois(3), logdBinPois(2) ); + if( b_i(i)==0 ) LogProb2_i(i) = logdBinPois(0); + if( b_i(i)==1 ) LogProb2_i(i) = logdBinPois(1); + if( b_i(i)==2 ) LogProb2_i(i) = logdBinPois(2); + if( b_i(i)==3 ) LogProb2_i(i) = logdBinPois(3); + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = 0; // Option not available + } + } + // Zero-inflated Lognormal Poisson + if(ObsModel_ez(e_i(i),0)==11){ + if( b_i(i)==0 ){ + //LogProb2_i(i) = log( (1-R1_i(i)) + dpois(Type(0.0), R2_i(i), false)*R1_i(i) ); // Pr[X=0] = 1-phi + Pois(X=0)*phi + LogProb2_i(i) = logspace_add( log(1-R1_i(i)), dpois(Type(0.0),R2_i(i)*exp(SigmaM(e_i(i),0)*delta_i(i)-0.5*pow(SigmaM(e_i(i),0),2)),true)+log(R1_i(i)) ); // Pr[X=0] = 1-phi + Pois(X=0)*phi + }else{ + LogProb2_i(i) = dpois(b_i(i), R2_i(i)*exp(SigmaM(e_i(i),0)*delta_i(i)-0.5*pow(SigmaM(e_i(i),0),2)), true) + log(R1_i(i)); // Pr[X=x] = Pois(X=x)*phi + } + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = rbinom( Type(1), R1_i(i) ); + if( b_i(i)>0 ){ + b_i(i) = rpois( R2_i(i)*exp(SigmaM(e_i(i),0)*delta_i(i)-0.5*pow(SigmaM(e_i(i),0),2)) ); + } + } + } + // Non-zero-inflated Poisson using log link from 1st linear predictor + if(ObsModel_ez(e_i(i),0)==12){ + LogProb2_i(i) = dpois(b_i(i), R1_i(i), true); + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = rpois( R1_i(i) ); + } + } + // Non-zero-inflated Bernoulli using cloglog link from 1st lilnear predict + if(ObsModel_ez(e_i(i),0)==13){ + if( b_i(i)==0 ){ + LogProb2_i(i) = dpois(Type(0), R1_i(i), true); + }else{ + LogProb2_i(i) = logspace_sub( log(Type(1.0)), dpois(Type(0), R1_i(i), true) ); + } + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = rpois( R1_i(i) ); + if( b_i(i)>0 ){ + b_i(i) = 1; + } + } + } + // Non-zero-inflated Lognormal-Poisson using log link from 1st linear predictor + if(ObsModel_ez(e_i(i),0)==14){ + LogProb2_i(i) = dpois(b_i(i), R1_i(i)*exp(SigmaM(e_i(i),0)*delta_i(i)-0.5*pow(SigmaM(e_i(i),0),2)), true); + // Simulate new values when using obj.simulate() + SIMULATE{ + b_i(i) = rpois( R1_i(i)*exp(SigmaM(e_i(i),0)*delta_i(i)-0.5*pow(SigmaM(e_i(i),0),2)) ); + } + } + } + } + REPORT( diag_iz ); + + // Joint likelihood + jnll_comp(10) = -1 * (LogProb1_i * (Type(1.0)-PredTF_i)).sum(); + jnll_comp(11) = -1 * (LogProb2_i * (Type(1.0)-PredTF_i)).sum(); + jnll = jnll_comp.sum(); + Type pred_jnll = -1 * ( LogProb1_i*PredTF_i + LogProb2_i*PredTF_i ).sum(); + REPORT( pred_jnll ); + REPORT( tmp_calc1 ); + REPORT( tmp_calc2 ); + + //////////////////////// + // Calculate index of abundance and density + //////////////////////// + + // Number of output-years + int n_y = t_yz.col(0).size(); + + // Predictive distribution -- ObsModel_ez(e,0)==4 isn't implemented (it had a bug previously) + Type a_average = a_i.sum()/a_i.size(); + array P1_gcy(n_g, n_c, n_y); + array R1_gcy(n_g, n_c, n_y); + array P2_gcy(n_g, n_c, n_y); + array R2_gcy(n_g, n_c, n_y); + array D_gcy(n_g, n_c, n_y); + for(c=0; c=0) & (t_yz(y,z) Index_gcyl(n_g, n_c, n_y, n_l); + array Index_cyl(n_c, n_y, n_l); + array ln_Index_cyl(n_c, n_y, n_l); + Index_cyl.setZero(); + for(int y=0; y Bratio_cyl(n_c, n_y, n_l); + array ln_Bratio_cyl(n_c, n_y, n_l); + for(c=0; c mean_Z_cym(n_c, n_y, n_m); + if( Options(2)==1 ){ + mean_Z_cym.setZero(); + int report_summary_TF = false; + for(c=0; c mean_D_cyl(n_c, n_y, n_l); + array log_mean_D_cyl(n_c, n_y, n_l); + mean_D_cyl.setZero(); + for(c=0; c effective_area_cyl(n_c, n_y, n_l); + array log_effective_area_cyl(n_c, n_y, n_l); + effective_area_cyl = Index_cyl / (mean_D_cyl/1000); // Correct for different units of Index and density + log_effective_area_cyl = log( effective_area_cyl ); + REPORT( effective_area_cyl ); + ADREPORT( effective_area_cyl ); + ADREPORT( log_effective_area_cyl ); + } + + // Reporting and standard-errors for covariance and correlation matrices + if( Options(5)==1 ){ + if( FieldConfig(0,0)>0 ){ + matrix L1_omega_cf = loadings_matrix( L_omega1_z, n_c, FieldConfig(0,0) ); + matrix lowercov_uppercor_omega1 = L1_omega_cf * L1_omega_cf.transpose(); + lowercov_uppercor_omega1 = convert_upper_cov_to_cor( lowercov_uppercor_omega1 ); + REPORT( lowercov_uppercor_omega1 ); + ADREPORT( lowercov_uppercor_omega1 ); + } + if( FieldConfig(1,0)>0 ){ + matrix L1_epsilon_cf = loadings_matrix( L_epsilon1_z, n_c, FieldConfig(1,0) ); + matrix lowercov_uppercor_epsilon1 = L1_epsilon_cf * L1_epsilon_cf.transpose(); + lowercov_uppercor_epsilon1 = convert_upper_cov_to_cor( lowercov_uppercor_epsilon1 ); + REPORT( lowercov_uppercor_epsilon1 ); + ADREPORT( lowercov_uppercor_epsilon1 ); + } + if( FieldConfig(2,0)>0 ){ + matrix L1_beta_cf = loadings_matrix( L_beta1_z, n_c, FieldConfig(2,0) ); + matrix lowercov_uppercor_beta1 = L1_beta_cf * L1_beta_cf.transpose(); + lowercov_uppercor_beta1 = convert_upper_cov_to_cor( lowercov_uppercor_beta1 ); + REPORT( lowercov_uppercor_beta1 ); + ADREPORT( lowercov_uppercor_beta1 ); + } + if( FieldConfig(0,1)>0 ){ + matrix L2_omega_cf = loadings_matrix( L_omega2_z, n_c, FieldConfig(0,1) ); + matrix lowercov_uppercor_omega2 = L2_omega_cf * L2_omega_cf.transpose(); + lowercov_uppercor_omega2 = convert_upper_cov_to_cor( lowercov_uppercor_omega2 ); + REPORT( lowercov_uppercor_omega2 ); + ADREPORT( lowercov_uppercor_omega2 ); + } + if( FieldConfig(1,1)>0 ){ + matrix L2_epsilon_cf = loadings_matrix( L_epsilon2_z, n_c, FieldConfig(1,1) ); + matrix lowercov_uppercor_epsilon2 = L2_epsilon_cf * L2_epsilon_cf.transpose(); + lowercov_uppercor_epsilon2 = convert_upper_cov_to_cor( lowercov_uppercor_epsilon2 ); + REPORT( lowercov_uppercor_epsilon2 ); + ADREPORT( lowercov_uppercor_epsilon2 ); + } + if( FieldConfig(2,1)>0 ){ + matrix L1_beta_cf = loadings_matrix( L_beta2_z, n_c, FieldConfig(2,1) ); + matrix lowercov_uppercor_beta2 = L1_beta_cf * L1_beta_cf.transpose(); + lowercov_uppercor_beta2 = convert_upper_cov_to_cor( lowercov_uppercor_beta2 ); + REPORT( lowercov_uppercor_beta2 ); + ADREPORT( lowercov_uppercor_beta2 ); + } + } + + // Synchrony + if( Options(6)==1 ){ + int n_z = yearbounds_zz.col(0).size(); + // Density ("D") or area-expanded total biomass ("B") for each category (use B when summing across sites) + matrix D_gy( n_g, n_y ); + matrix B_cy( n_c, n_y ); + vector B_y( n_y ); + D_gy.setZero(); + B_cy.setZero(); + B_y.setZero(); + // Sample variance in category-specific density ("D") and biomass ("B") + array varD_gcz( n_g, n_c, n_z ); + array varD_gz( n_g, n_z ); + array varB_cz( n_c, n_z ); + vector varB_z( n_z ); + vector varB_gbar_z( n_z ); + vector varB_cbar_z( n_z ); + vector ln_varB_z( n_z ); + vector ln_varB_gbar_z( n_z ); + vector ln_varB_cbar_z( n_z ); + array maxsdD_gz( n_g, n_z ); + array maxsdB_cz( n_c, n_z ); + vector maxsdB_z( n_z ); + varD_gcz.setZero(); + varD_gz.setZero(); + varB_cz.setZero(); + varB_z.setZero(); + varB_gbar_z.setZero(); + varB_cbar_z.setZero(); + maxsdD_gz.setZero(); + maxsdB_cz.setZero(); + maxsdB_z.setZero(); + // Proportion of total biomass ("P") for each location or each category + matrix propB_gz( n_g, n_z ); + matrix propB_cz( n_c, n_z ); + propB_gz.setZero(); + propB_cz.setZero(); + // Synchrony indices + matrix phi_gz( n_g, n_z ); + matrix phi_cz( n_c, n_z ); + vector phi_gbar_z( n_z ); + vector phi_cbar_z( n_z ); + vector phi_z( n_z ); + phi_gbar_z.setZero(); + phi_cbar_z.setZero(); + phi_z.setZero(); + // Calculate total biomass for different categories + for( int y=0; y CovHat( n_c, n_c ); + matrix CovHat( n_c, n_c ); + CovHat.setIdentity(); + CovHat *= pow(0.0001, 2); + if( FieldConfig(1,0)>0 ) CovHat += loadings_matrix(L_epsilon1_z, n_c, FieldConfig(1,0)) * loadings_matrix(L_epsilon1_z, n_c, FieldConfig(1,0)).transpose(); + if( FieldConfig(1,1)>0 ) CovHat += loadings_matrix(L_epsilon2_z, n_c, FieldConfig(1,1)) * loadings_matrix(L_epsilon2_z, n_c, FieldConfig(1,1)).transpose(); + // Coherence ranges from 0 (all factors are equal) to 1 (first factor explains all variance) + SelfAdjointEigenSolver > es(CovHat); + vector eigenvalues_c = es.eigenvalues(); // Ranked from lowest to highest for some reason + Type psi = 0; + for(c=0; c diag_CovHat( n_c ); + vector log_diag_CovHat( n_c ); + for(c=0; c PropIndex_cyl(n_c, n_y, n_l); + array ln_PropIndex_cyl(n_c, n_y, n_l); + Type sumtemp; + for(int y=0; y D_i( n_i ); + D_i = R1_i * R2_i; + ADREPORT( D_i ); + } + + return jnll; +} From 920c6176066a9bbe1ee26dc924207d1b39829b01 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Mon, 4 Mar 2019 18:14:35 -0800 Subject: [PATCH 07/31] Bug fix in V8.0.0... ... which was crashing for multispecies models due to problems with Xi_izp; now fixed. --- R/make_data.R | 3 +++ inst/executables/VAST_v8_0_0.cpp | 21 ++++++++++--------- tests/testthat/test-comp-expansion.R | 3 +-- tests/testthat/test-multi-species-examples.R | 6 ++---- tests/testthat/test-single-species-examples.R | 12 ++++------- 5 files changed, 21 insertions(+), 24 deletions(-) diff --git a/R/make_data.R b/R/make_data.R index 7676702..03dfbf6 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -320,6 +320,9 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), # Check for incompatibilities amongst versions ################### + if( FishStatsUtils::convert_version_name(Version) >= FishStatsUtils::convert_version_name("VAST_v8_0_0") ){ + if( is.null(spatial_list) ) stop("Must provide `spatial_list` for Version >= 8.0.0") + } if( any(ObsModel_ez[,2]==1) ){ # Versions 1.6.0 through 2.2.0 used a previous interpretation of area-swept for Poisson-link model and are not consistent with Q-Q plotting if( FishStatsUtils::convert_version_name(Version) <= FishStatsUtils::convert_version_name("VAST_v2_2_0") ){ diff --git a/inst/executables/VAST_v8_0_0.cpp b/inst/executables/VAST_v8_0_0.cpp index bd599be..139a530 100644 --- a/inst/executables/VAST_v8_0_0.cpp +++ b/inst/executables/VAST_v8_0_0.cpp @@ -141,7 +141,7 @@ matrix covariation_by_category_nll( int n_f, int n_j, int n_c, matrix // matrix convert_upper_cov_to_cor( matrix cov ){ - int nrow = cov.row(0).size(); + int nrow = cov.rows(); for( int i=0; i::operator() () Options_vec = Options_list.Options_vec; vector Options( Options_list.Options.size() ); Options = Options_list.Options; - matrix yearbounds_zz( Options_list.yearbounds_zz.col(0).size(), 2 ); + matrix yearbounds_zz( Options_list.yearbounds_zz.rows(), 2 ); yearbounds_zz = Options_list.yearbounds_zz; matrix Expansion_cz( n_c, 2 ); Expansion_cz = Options_list.Expansion_cz; @@ -1226,7 +1226,8 @@ Type objective_function::operator() () for( int zt=0; zt=0) & (c_iz(i,zc)=0) & (t_iz(i,zt)::operator() () Type tmp_calc1; Type tmp_calc2; // Linear predictor (pre-link) for presence/absence component - matrix P1_iz(n_i,c_iz.row(0).size()); + matrix P1_iz(n_i,c_iz.cols()); // Response predictor (post-link) // ObsModel_ez(e,0) = 0:4 or 11:12: probability ("phi") that data is greater than zero // ObsModel_ez(e,0) = 5 (ZINB): phi = 1-ZeroInflation_prob -> Pr[D=0] = NB(0|mu,var)*phi + (1-phi) -> Pr[D>0] = phi - NB(0|mu,var)*phi @@ -1249,7 +1250,7 @@ Type objective_function::operator() () vector log_R1_i(n_i); vector LogProb1_i(n_i); // Linear predictor (pre-link) for positive component - matrix P2_iz(n_i,c_iz.row(0).size()); + matrix P2_iz(n_i,c_iz.cols()); // Response predictor (post-link) // ObsModel_ez(e,0) = 0:3, 11:12: expected value of data, given that data is greater than zero -> E[D] = mu*phi // ObsModel_ez(e,0) = 4 (ZANB): expected value ("mu") of neg-bin PRIOR to truncating Pr[D=0] -> E[D] = mu/(1-NB(0|mu,var))*phi ALSO Pr[D] = NB(D|mu,var)/(1-NB(0|mu,var))*phi @@ -1273,7 +1274,7 @@ Type objective_function::operator() () if( (c_iz(i,zc)>=0) & (c_iz(i,zc)=0) & (t_iz(i,zt)::operator() () // P2_i: Log-average weight; R2_i: Positive density prediction tmp_calc1 = 0; tmp_calc2 = 0; - for( int zc=0; zc=0) & (c_iz(i,zc)::operator() () //////////////////////// // Number of output-years - int n_y = t_yz.col(0).size(); + int n_y = t_yz.rows(); // Predictive distribution -- ObsModel_ez(e,0)==4 isn't implemented (it had a bug previously) Type a_average = a_i.sum()/a_i.size(); @@ -1528,7 +1529,7 @@ Type objective_function::operator() () // Calculate linear predictors P1_gcy(g,c,y) = Omega1_gc(g,c); P2_gcy(g,c,y) = Omega2_gc(g,c); - for( int z=0; z=0) & (t_yz(y,z)::operator() () // Synchrony if( Options(6)==1 ){ - int n_z = yearbounds_zz.col(0).size(); + int n_z = yearbounds_zz.rows(); // Density ("D") or area-expanded total biomass ("B") for each category (use B when summing across sites) matrix D_gy( n_g, n_y ); matrix B_cy( n_c, n_y ); diff --git a/tests/testthat/test-comp-expansion.R b/tests/testthat/test-comp-expansion.R index 9fb71b2..7dee6b9 100644 --- a/tests/testthat/test-comp-expansion.R +++ b/tests/testthat/test-comp-expansion.R @@ -13,8 +13,7 @@ test_that("Male lingcod compositional expansion is working ", { # Run model Extrapolation_List = make_extrapolation_info( Region="California_current", strata.limits=strata.limits ) Spatial_List = make_spatial_info( grid_size_km=grid_size_km, n_x=n_x, Method=Method, Lon=Data_Geostat[,'BEST_LON_DD'], Lat=Data_Geostat[,'BEST_LAT_DD'], Extrapolation_List=Extrapolation_List, DirPath=test_path ) - Data_Geostat = cbind( Data_Geostat, "knot_i"=Spatial_List$knot_i ) - TmbData = VAST::Data_Fn("CheckForErrors"=FALSE, "Version"=Version_VAST, "OverdispersionConfig"=OverdispersionConfig, "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=ObsModel_Est, "c_i"=as.numeric(Data_Geostat[,'Length_bin'])-1, "b_i"=Data_Geostat[,'First_stage_expanded_numbers'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=rep(0,nrow(Data_Geostat)), "s_i"=Data_Geostat[,'knot_i']-1, "t_i"=Data_Geostat[,'Year'], "a_xl"=Spatial_List$a_xl, "MeshList"=Spatial_List$MeshList, "GridList"=Spatial_List$GridList, "Method"=Spatial_List$Method, "Aniso"=Aniso ) + TmbData = VAST::Data_Fn("CheckForErrors"=FALSE, "Version"=Version_VAST, "OverdispersionConfig"=OverdispersionConfig, "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=ObsModel_Est, "c_i"=as.numeric(Data_Geostat[,'Length_bin'])-1, "b_i"=Data_Geostat[,'First_stage_expanded_numbers'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=rep(0,nrow(Data_Geostat)), "t_i"=Data_Geostat[,'Year'], "spatial_list"=Spatial_List, "Aniso"=Aniso ) # Switch missing categories to NA EncProb_tc = tapply( TmbData$b_i, INDEX=list(TmbData$t_i,TmbData$c_i), FUN=function(vec){mean(vec>0)} ) TmbData$b_i = ifelse( EncProb_tc[1+cbind(TmbData$t_i,TmbData$c_i)]==0, NA, TmbData$b_i ) diff --git a/tests/testthat/test-multi-species-examples.R b/tests/testthat/test-multi-species-examples.R index 231a85d..9dcca64 100644 --- a/tests/testthat/test-multi-species-examples.R +++ b/tests/testthat/test-multi-species-examples.R @@ -17,8 +17,7 @@ test_that("Eastern Bering Sea 3-species is working ", { load( file.path(test_path,"Data_Geostat.RData") ) Extrapolation_List = make_extrapolation_info( Region=Region, strata.limits=strata.limits ) Spatial_List = make_spatial_info( grid_size_km=grid_size_km, n_x=n_x, Method=Method, Lon=Data_Geostat[,'Lon'], Lat=Data_Geostat[,'Lat'], Extrapolation_List=Extrapolation_List, randomseed=Kmeans_Config[["randomseed"]], nstart=Kmeans_Config[["nstart"]], iter.max=Kmeans_Config[["iter.max"]], DirPath=test_path ) - Data_Geostat = cbind( Data_Geostat, "knot_i"=Spatial_List$knot_i ) - TmbData = VAST::Data_Fn("Version"=Version_VAST, "OverdispersionConfig"=rep(VesselConfig[2],2), "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=ObsModel, "c_i"=Data_Geostat[,'spp']-1, "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(factor(paste(Data_Geostat[,'Vessel'],Data_Geostat[,'Year'])))-1, "s_i"=Data_Geostat[,'knot_i']-1, "t_i"=Data_Geostat[,'Year'], "a_xl"=Spatial_List$a_xl, "MeshList"=Spatial_List$MeshList, "GridList"=Spatial_List$GridList, "Method"=Spatial_List$Method ) + TmbData = VAST::Data_Fn("Version"=Version_VAST, "OverdispersionConfig"=rep(VesselConfig[2],2), "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=ObsModel, "c_i"=Data_Geostat[,'spp']-1, "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(factor(paste(Data_Geostat[,'Vessel'],Data_Geostat[,'Year'])))-1, "t_i"=Data_Geostat[,'Year'], "spatial_list"=Spatial_List ) TmbList = VAST::Build_TMB_Fn("build_model"=TRUE, "TmbData"=TmbData, "RunDir"=test_path, "Version"=Version_VAST, "RhoConfig"=RhoConfig, "loc_x"=Spatial_List$loc_x) on.exit( dyn.unload(paste0(test_path,"/",TMB::dynlib(Version_VAST))), add=TRUE ) Opt = TMBhelper::Optimize( obj=TmbList[["Obj"]], getsd=FALSE, lower=TmbList[["Lower"]], upper=TmbList[["Upper"]] ) @@ -41,8 +40,7 @@ test_that("Eastern Bering Sea 5-species is working ", { load( file.path(test_path,"Data_Geostat.RData") ) Extrapolation_List = make_extrapolation_info( Region=Region, strata.limits=strata.limits ) Spatial_List = make_spatial_info( grid_size_km=grid_size_km, n_x=n_x, Method=Method, Lon=Data_Geostat[,'Lon'], Lat=Data_Geostat[,'Lat'], Extrapolation_List=Extrapolation_List, randomseed=Kmeans_Config[["randomseed"]], nstart=Kmeans_Config[["nstart"]], iter.max=Kmeans_Config[["iter.max"]], DirPath=test_path ) - Data_Geostat = cbind( Data_Geostat, "knot_i"=Spatial_List$knot_i ) - TmbData = VAST::Data_Fn("Version"=Version_VAST, "OverdispersionConfig"=rep(VesselConfig[2],2), "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=ObsModel, "c_i"=Data_Geostat[,'spp']-1, "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(factor(paste(Data_Geostat[,'Vessel'],Data_Geostat[,'Year'])))-1, "s_i"=Data_Geostat[,'knot_i']-1, "t_i"=Data_Geostat[,'Year'], "a_xl"=Spatial_List$a_xl, "MeshList"=Spatial_List$MeshList, "GridList"=Spatial_List$GridList, "Method"=Spatial_List$Method ) + TmbData = VAST::Data_Fn("Version"=Version_VAST, "OverdispersionConfig"=rep(VesselConfig[2],2), "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=ObsModel, "c_i"=Data_Geostat[,'spp']-1, "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(factor(paste(Data_Geostat[,'Vessel'],Data_Geostat[,'Year'])))-1, "t_i"=Data_Geostat[,'Year'], "spatial_list"=Spatial_List ) TmbList = VAST::Build_TMB_Fn("TmbData"=TmbData, "RunDir"=test_path, "Version"=Version_VAST, "RhoConfig"=RhoConfig, "loc_x"=Spatial_List$loc_x) on.exit( dyn.unload(paste0(test_path,"/",TMB::dynlib(Version_VAST))), add=TRUE ) Opt = TMBhelper::Optimize( obj=TmbList[["Obj"]], getsd=FALSE, lower=TmbList[["Lower"]], upper=TmbList[["Upper"]] ) diff --git a/tests/testthat/test-single-species-examples.R b/tests/testthat/test-single-species-examples.R index 9f29e9c..4cb9cbe 100644 --- a/tests/testthat/test-single-species-examples.R +++ b/tests/testthat/test-single-species-examples.R @@ -29,8 +29,7 @@ test_that("Eastern Bering Sea pollock is working ", { Data_Geostat = data.frame( "Catch_KG"=EBS_pollock_data[,'catch'], "Year"=EBS_pollock_data[,'year'], "Vessel"="missing", "AreaSwept_km2"=0.01, "Lat"=EBS_pollock_data[,'lat'], "Lon"=EBS_pollock_data[,'long'], "Pass"=0) Extrapolation_List = make_extrapolation_info( Region=Region, strata.limits=strata.limits ) Spatial_List = make_spatial_info( grid_size_km=grid_size_km, n_x=n_x, Method=Method, Lon=Data_Geostat[,'Lon'], Lat=Data_Geostat[,'Lat'], Extrapolation_List=Extrapolation_List, randomseed=Kmeans_Config[["randomseed"]], nstart=Kmeans_Config[["nstart"]], iter.max=Kmeans_Config[["iter.max"]], DirPath=test_path ) - Data_Geostat = cbind( Data_Geostat, "knot_i"=Spatial_List$knot_i ) - TmbData = VAST::Data_Fn("Version"=Version_VAST, "OverdispersionConfig"=rep(VesselConfig[2],2), "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=c(ObsModel,0), "c_i"=rep(0,nrow(Data_Geostat)), "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(factor(paste(Data_Geostat[,'Vessel'],Data_Geostat[,'Year'])))-1, "s_i"=Data_Geostat[,'knot_i']-1, "t_i"=Data_Geostat[,'Year'], "a_xl"=Spatial_List$a_xl, "MeshList"=Spatial_List$MeshList, "GridList"=Spatial_List$GridList, "Method"=Spatial_List$Method ) + TmbData = VAST::Data_Fn("Version"=Version_VAST, "OverdispersionConfig"=rep(VesselConfig[2],2), "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=c(ObsModel,0), "c_i"=rep(0,nrow(Data_Geostat)), "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(factor(paste(Data_Geostat[,'Vessel'],Data_Geostat[,'Year'])))-1, "t_i"=Data_Geostat[,'Year'], "spatial_list"=Spatial_List ) TmbList = VAST::Build_TMB_Fn("TmbData"=TmbData, "build_model"=TRUE, "RunDir"=test_path, "Version"=Version_VAST, "RhoConfig"=RhoConfig, "loc_x"=Spatial_List$loc_x) on.exit( dyn.unload(paste0(test_path,"/",TMB::dynlib(Version_VAST))), add=TRUE ) Opt = TMBhelper::Optimize( obj=TmbList[["Obj"]], getsd=FALSE, lower=TmbList[["Lower"]], upper=TmbList[["Upper"]] ) # , rel.tol=1e-20 @@ -54,8 +53,7 @@ test_that("Chatham Rise hake is working ", { Data_Geostat = data.frame( "Catch_KG"=chatham_rise_hake[,'Hake_kg_per_km2'], "Year"=chatham_rise_hake[,'Year'], "Vessel"=1, "AreaSwept_km2"=1, "Lat"=chatham_rise_hake[,'Lat'], "Lon"=chatham_rise_hake[,'Lon']) Extrapolation_List = make_extrapolation_info( Region=Region, strata.limits=strata.limits ) Spatial_List = make_spatial_info( grid_size_km=grid_size_km, n_x=n_x, Method=Method, Lon=Data_Geostat[,'Lon'], Lat=Data_Geostat[,'Lat'], Extrapolation_List=Extrapolation_List, randomseed=Kmeans_Config[["randomseed"]], nstart=Kmeans_Config[["nstart"]], iter.max=Kmeans_Config[["iter.max"]], DirPath=test_path ) - Data_Geostat = cbind( Data_Geostat, "knot_i"=Spatial_List$knot_i ) - TmbData = VAST::Data_Fn("Version"=Version_VAST, "OverdispersionConfig"=rep(VesselConfig[2],2), "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=c(ObsModel,0), "c_i"=rep(0,nrow(Data_Geostat)), "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(factor(paste(Data_Geostat[,'Vessel'],Data_Geostat[,'Year'])))-1, "s_i"=Data_Geostat[,'knot_i']-1, "t_i"=Data_Geostat[,'Year'], "a_xl"=Spatial_List$a_xl, "MeshList"=Spatial_List$MeshList, "GridList"=Spatial_List$GridList, "Method"=Spatial_List$Method ) + TmbData = VAST::Data_Fn("Version"=Version_VAST, "OverdispersionConfig"=rep(VesselConfig[2],2), "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=c(ObsModel,0), "c_i"=rep(0,nrow(Data_Geostat)), "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(factor(paste(Data_Geostat[,'Vessel'],Data_Geostat[,'Year'])))-1, "t_i"=Data_Geostat[,'Year'], "spatial_list"=Spatial_List ) TmbList = VAST::Build_TMB_Fn("TmbData"=TmbData, "RunDir"=test_path, "Version"=Version_VAST, "RhoConfig"=RhoConfig, "loc_x"=Spatial_List$loc_x) on.exit( dyn.unload(paste0(test_path,"/",TMB::dynlib(Version_VAST))), add=TRUE ) Opt = TMBhelper::Optimize( obj=TmbList[["Obj"]], getsd=FALSE, lower=TmbList[["Lower"]], upper=TmbList[["Upper"]] ) # , rel.tol=1e-20 @@ -79,8 +77,7 @@ test_that("West Coast groundfish bottom trawl survey, canary rockfish is working Data_Geostat = data.frame( "Catch_KG"=WCGBTS_Canary_example[,'HAUL_WT_KG'], "Year"=as.numeric(sapply(WCGBTS_Canary_example[,'PROJECT_CYCLE'],FUN=function(Char){strsplit(as.character(Char)," ")[[1]][2]})), "Vessel"=WCGBTS_Canary_example[,"VESSEL"], "AreaSwept_km2"=WCGBTS_Canary_example[,"AREA_SWEPT_HA"]/1e2, "Lat"=WCGBTS_Canary_example[,'BEST_LAT_DD'], "Lon"=WCGBTS_Canary_example[,'BEST_LON_DD'], "Pass"=WCGBTS_Canary_example[,'PASS']-1.5) Extrapolation_List = make_extrapolation_info( Region=Region, strata.limits=strata.limits ) Spatial_List = make_spatial_info( grid_size_km=grid_size_km, n_x=n_x, Method=Method, Lon=Data_Geostat[,'Lon'], Lat=Data_Geostat[,'Lat'], Extrapolation_List=Extrapolation_List, randomseed=Kmeans_Config[["randomseed"]], nstart=Kmeans_Config[["nstart"]], iter.max=Kmeans_Config[["iter.max"]], DirPath=test_path ) - Data_Geostat = cbind( Data_Geostat, "knot_i"=Spatial_List$knot_i ) - TmbData = VAST::Data_Fn("Version"=Version_VAST, "OverdispersionConfig"=rep(VesselConfig[2],2), "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=c(ObsModel,0), "c_i"=rep(0,nrow(Data_Geostat)), "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(factor(paste(Data_Geostat[,'Vessel'],Data_Geostat[,'Year'])))-1, "s_i"=Data_Geostat[,'knot_i']-1, "t_i"=Data_Geostat[,'Year'], "a_xl"=Spatial_List$a_xl, "MeshList"=Spatial_List$MeshList, "GridList"=Spatial_List$GridList, "Method"=Spatial_List$Method ) + TmbData = VAST::Data_Fn("Version"=Version_VAST, "OverdispersionConfig"=rep(VesselConfig[2],2), "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=c(ObsModel,0), "c_i"=rep(0,nrow(Data_Geostat)), "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(factor(paste(Data_Geostat[,'Vessel'],Data_Geostat[,'Year'])))-1, "t_i"=Data_Geostat[,'Year'], "spatial_list"=Spatial_List ) TmbList = VAST::Build_TMB_Fn("TmbData"=TmbData, "RunDir"=test_path, "Version"=Version_VAST, "RhoConfig"=RhoConfig, "loc_x"=Spatial_List$loc_x) on.exit( dyn.unload(paste0(test_path,"/",TMB::dynlib(Version_VAST))), add=TRUE ) Opt = TMBhelper::Optimize( obj=TmbList[["Obj"]], getsd=FALSE, lower=TmbList[["Lower"]], upper=TmbList[["Upper"]] ) # , rel.tol=1e-20 @@ -105,8 +102,7 @@ test_that("Aleutian Islands groundfish bottom trawl survey, POP is working ", { Data_Geostat = data.frame( "Catch_KG"=AI_pacific_ocean_perch[,'cpue..kg.km.2.'], "Year"=AI_pacific_ocean_perch[,'year'], "Vessel"="missing", "AreaSwept_km2"=1, "Lat"=AI_pacific_ocean_perch[,'start.latitude'], "Lon"=AI_pacific_ocean_perch[,'start.longitude'], "Pass"=0) Extrapolation_List = make_extrapolation_info( Region=Region, strata.limits=strata.limits ) Spatial_List = make_spatial_info( grid_size_km=grid_size_km, n_x=n_x, Method=Method, Lon=Data_Geostat[,'Lon'], Lat=Data_Geostat[,'Lat'], Extrapolation_List=Extrapolation_List, randomseed=Kmeans_Config[["randomseed"]], nstart=Kmeans_Config[["nstart"]], iter.max=Kmeans_Config[["iter.max"]], DirPath=test_path ) - Data_Geostat = cbind( Data_Geostat, "knot_i"=Spatial_List$knot_i ) - TmbData = VAST::Data_Fn("Version"=Version_VAST, "OverdispersionConfig"=rep(VesselConfig[2],2), "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=c(ObsModel,0), "c_i"=rep(0,nrow(Data_Geostat)), "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(factor(paste(Data_Geostat[,'Vessel'],Data_Geostat[,'Year'])))-1, "s_i"=Data_Geostat[,'knot_i']-1, "t_i"=Data_Geostat[,'Year'], "a_xl"=Spatial_List$a_xl, "MeshList"=Spatial_List$MeshList, "GridList"=Spatial_List$GridList, "Method"=Spatial_List$Method ) + TmbData = VAST::Data_Fn("Version"=Version_VAST, "OverdispersionConfig"=rep(VesselConfig[2],2), "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=c(ObsModel,0), "c_i"=rep(0,nrow(Data_Geostat)), "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(factor(paste(Data_Geostat[,'Vessel'],Data_Geostat[,'Year'])))-1, "t_i"=Data_Geostat[,'Year'], "spatial_list"=Spatial_List ) TmbList = VAST::Build_TMB_Fn("TmbData"=TmbData, "RunDir"=test_path, "Version"=Version_VAST, "RhoConfig"=RhoConfig, "loc_x"=Spatial_List$loc_x) on.exit( dyn.unload(paste0(test_path,"/",TMB::dynlib(Version_VAST))), add=TRUE ) Opt = TMBhelper::Optimize( obj=TmbList[["Obj"]], getsd=FALSE, lower=TmbList[["Lower"]], upper=TmbList[["Upper"]] ) # , rel.tol=1e-20 From cb9cda673173030f19645a5c6dd9cbddf88e4953 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Tue, 5 Mar 2019 10:16:30 -0800 Subject: [PATCH 08/31] small fixes for V8.0.0 --- R/make_data.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/make_data.R b/R/make_data.R index 03dfbf6..677afff 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -253,7 +253,7 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), Z_gm = Z_xm = matrix(0, nrow=nrow(a_gl), ncol=ncol(a_gl) ) # Size so that it works for Version 3g-3j }else{ - Z_xm = MeshList$loc_x + Z_xm = spatial_list$loc_x Z_gm = spatial_list$loc_g message( "Calculating range shift for stratum #1:",colnames(a_gl[1])) } @@ -559,7 +559,7 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=FieldConfig_input, "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "Xconfig_zcp"=Xconfig_zcp, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v8_0_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_g"=n_g, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=FieldConfig_input, "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "Xconfig_zcp"=Xconfig_zcp, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_gl"=a_gl, "X_itp"=X_itp, "X_gtp"=X_gtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_gm"=Z_gm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2, "Ais_ij"=cbind(spatial_list$A_is@i,spatial_list$A_is@j), "Ais_x"=spatial_list$A_is@x, "Ags_ij"=cbind(spatial_list$A_gs@i,spatial_list$A_gs@j), "Ags_x"=spatial_list$A_gs@x ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_g"=n_g, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_gm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=FieldConfig_input, "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "Xconfig_zcp"=Xconfig_zcp, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_gl"=a_gl, "X_itp"=X_itp, "X_gtp"=X_gtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_gm"=Z_gm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2, "Ais_ij"=cbind(spatial_list$A_is@i,spatial_list$A_is@j), "Ais_x"=spatial_list$A_is@x, "Ags_ij"=cbind(spatial_list$A_gs@i,spatial_list$A_gs@j), "Ags_x"=spatial_list$A_gs@x ) } if( is.null(Return) ) stop("`Version` provided does not match the list of possible values") if( "spde" %in% names(Return) ) Return[['spde']] = MeshList$isotropic_spde$param.inla[c("M0","M1","M2")] From 1f88f338af270a1bd07e6cf23ea3985268c60e73 Mon Sep 17 00:00:00 2001 From: "James.Thorson" <1454196437@mil> Date: Thu, 7 Mar 2019 14:26:00 -0800 Subject: [PATCH 09/31] Improve documentation for VAM... settings in V8.0.0 --- R/make_data.R | 9 ++++++--- inst/executables/VAST_v8_0_0.cpp | 1 + man/make_data.Rd | 34 +++++++++++++++++++++----------- 3 files changed, 29 insertions(+), 15 deletions(-) diff --git a/R/make_data.R b/R/make_data.R index 677afff..4d127a6 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -35,14 +35,17 @@ #' \item{ObsModel_ez[e,2]=4}{Poisson-link delta-model, but fixing encounter probability=1 for any year where all samples encounter the species and encounter probability=0 for any year where no samples encounter the species} #' } #' @param RhoConfig OPTIONAL, vector of form c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0) specifying whether either intercepts (Beta1 and Beta2) or spatio-temporal variation (Epsilon1 and Epsilon2) is structured among time intervals (0: each year as fixed effect; 1: each year as random following IID distribution; 2: each year as random following a random walk; 3: constant among years as fixed effect; 4: each year as random following AR1 process) -#' @param VamConfig Options to estimate interactions, where first slot selects method for forming interaction matrix, the second indicates rank, the third indicates whether to incorporate effect of \code{F_ct}, and the fourth indicates whether to add a new "t=0" year (while incrementing all \code{t_i} inputs) which represents B0 +#' @param VamConfig Options to estimate interactions, containing three slots: +#' \describe{ +#' \item{VamConfig[0]}{selects method for forming interaction matrix; Turn off feature using 0, or I recommend using 2 by default} +#' \item{VamConfig[1]}{indicates the rank of the interaction matrix, indicating the number of community axes that have regulated dynamics} +#' \item{VamConfig[2]}{Indicates whether interactions occur before spatio-temporal variation (\code{VamConfig[2]=0}) or after \code{VamConfig[2]=1}} +#' } #' @param spatial_list tagged list of locatoinal information from , i.e., from \code{FishStatsUtils::make_spatial_info} - #' @param a_xl Area associated with each knot #' @param MeshList, tagged list representing location information for the SPDE mesh hyperdistribution, i.e., from \code{SpatialDeltaGLMM::Spatial_Information_Fn} #' @param GridList, tagged list representing location information for the 2D AR1 grid hyperdistribution, i.e., from \code{SpatialDeltaGLMM::Spatial_Information_Fn} #' @param Method, character (either "Mesh" or "Grid") specifying hyperdistribution (Default="Mesh") - #' @param PredTF_i OPTIONAL, whether each observation i is included in the likelihood (PredTF_i[i]=0) or in the predictive probability (PredTF_i[i]=1) #' @param X_xtp OPTIONAL, array of dynamic (varying among time intervals) density covariates #' @param Xconfig_zcp OPTIONAL, 3D array of settings for each dynamic density covariate, where the first dimension corresponds to 1st or 2nd linear predictors, second dimension corresponds to model category, and third dimension corresponds to each density covariate diff --git a/inst/executables/VAST_v8_0_0.cpp b/inst/executables/VAST_v8_0_0.cpp index 139a530..cb167cc 100644 --- a/inst/executables/VAST_v8_0_0.cpp +++ b/inst/executables/VAST_v8_0_0.cpp @@ -480,6 +480,7 @@ Type objective_function::operator() () DATA_IVECTOR(VamConfig); // Slot 0 -- method for calculating n_c-by-n_c interaction matrix, B_ff // Slot 1 -- rank of interaction matrix B_ff + // Slot 2 -- Timing of interactions; 0=Before correlated dynamics; 1=After correlated dynamics // Current implementation only makes sense when (1) intercepts are constant among years; (2) using a Poisson-link delta model; (3) n_f=n_c for spatio-temporal variation; (4) starts near equilibrium manifold DATA_IARRAY(Xconfig_zcp); // Row 0 -- Methods for 1st component for each covariate in X_xtp (0=Off; 1=Estimate; 2=Estimate with spatially varying coefficient) diff --git a/man/make_data.Rd b/man/make_data.Rd index 577f5bb..4fa782c 100644 --- a/man/make_data.Rd +++ b/man/make_data.Rd @@ -4,18 +4,21 @@ \alias{make_data} \title{Build data input for VAST model} \usage{ -make_data(b_i, a_i, c_iz, s_i, t_iz, e_i = c_iz[, 1], v_i = rep(0, +make_data(b_i, a_i, c_iz, t_iz, e_i = c_iz[, 1], v_i = rep(0, length(b_i)), Version, FieldConfig, OverdispersionConfig = c(eta1 = 0, eta2 = 0), ObsModel_ez = c(PosDist = 1, Link = 0), RhoConfig = c(Beta1 = 0, Beta2 = 0, Epsilon1 = 0, Epsilon2 = 0), - VamConfig = c(Method = 0, Rank = 0, Timing = 0), a_xl, MeshList, - GridList, Method, Aniso = TRUE, PredTF_i = rep(0, length(b_i)), - X_xj = NULL, X_xtp = NULL, Xconfig_zcp = NULL, Q_ik = NULL, - Network_sz = NULL, F_ct = NULL, F_init = 1, t_yz = NULL, - CheckForErrors = TRUE, yearbounds_zz = NULL, - Options = c(SD_site_logdensity = 0, Calculate_Range = 0, - Calculate_effective_area = 0, Calculate_Cov_SE = 0, Calculate_Synchrony = - 0, Calculate_proportion = 0), Expansion_cz = NULL) + VamConfig = c(Method = 0, Rank = 0, Timing = 0), spatial_list = NULL, + a_xl = NULL, MeshList = spatial_list$MeshList, + GridList = spatial_list$GridList, Method = spatial_list$Method, + s_i = spatial_list$knot_i - 1, Aniso = TRUE, PredTF_i = rep(0, + length(b_i)), X_xj = NULL, X_xtp = NULL, Xconfig_zcp = NULL, + X_gtp = NULL, X_itp = NULL, Q_ik = NULL, Network_sz = NULL, + F_ct = NULL, F_init = 1, t_yz = NULL, CheckForErrors = TRUE, + yearbounds_zz = NULL, Options = c(SD_site_logdensity = 0, + Calculate_Range = 0, Calculate_effective_area = 0, Calculate_Cov_SE = 0, + Calculate_Synchrony = 0, Calculate_proportion = 0), + Expansion_cz = NULL) } \arguments{ \item{b_i}{Sampled biomass for each observation i} @@ -24,8 +27,6 @@ make_data(b_i, a_i, c_iz, s_i, t_iz, e_i = c_iz[, 1], v_i = rep(0, \item{c_iz}{Category (e.g., species, length-bin) for each observation i} -\item{s_i}{Spatial knot (e.g., grid cell) for each observation i} - \item{t_iz}{Matrix where each row species the time for each observation i (if t_iz is a vector, it is coerced to a matrix with one column; if it is a matrix with two or more columns, it specifies multiple times for each observation, e.g., both year and season)} \item{e_i}{Error distribution for each observation i (by default \code{e_i=c_i})} @@ -62,7 +63,14 @@ make_data(b_i, a_i, c_iz, s_i, t_iz, e_i = c_iz[, 1], v_i = rep(0, \item{RhoConfig}{OPTIONAL, vector of form c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0) specifying whether either intercepts (Beta1 and Beta2) or spatio-temporal variation (Epsilon1 and Epsilon2) is structured among time intervals (0: each year as fixed effect; 1: each year as random following IID distribution; 2: each year as random following a random walk; 3: constant among years as fixed effect; 4: each year as random following AR1 process)} -\item{VamConfig}{Options to estimate interactions, where first slot selects method for forming interaction matrix, the second indicates rank, the third indicates whether to incorporate effect of \code{F_ct}, and the fourth indicates whether to add a new "t=0" year (while incrementing all \code{t_i} inputs) which represents B0} +\item{VamConfig}{Options to estimate interactions, containing three slots: +\describe{ + \item{VamConfig[0]}{selects method for forming interaction matrix; Turn off feature using 0, or I recommend using 2 by default} + \item{VamConfig[1]}{indicates the rank of the interaction matrix, indicating the number of community axes that have regulated dynamics} + \item{VamConfig[2]}{Indicates whether interactions occur before spatio-temporal variation (\code{VamConfig[2]=0}) or after \code{VamConfig[2]=1}} +}} + +\item{spatial_list}{tagged list of locatoinal information from , i.e., from \code{FishStatsUtils::make_spatial_info}} \item{a_xl}{Area associated with each knot} @@ -72,6 +80,8 @@ make_data(b_i, a_i, c_iz, s_i, t_iz, e_i = c_iz[, 1], v_i = rep(0, \item{Method, }{character (either "Mesh" or "Grid") specifying hyperdistribution (Default="Mesh")} +\item{s_i}{Spatial knot (e.g., grid cell) for each observation i} + \item{Aniso}{OPTIONAL, whether to assume isotropy (Aniso=0) or geometric anisotropy (Aniso=1)} \item{PredTF_i}{OPTIONAL, whether each observation i is included in the likelihood (PredTF_i[i]=0) or in the predictive probability (PredTF_i[i]=1)} From a78661a02f5138d416ae11e42e135747bb8ac5d4 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Fri, 8 Mar 2019 14:17:08 -0800 Subject: [PATCH 10/31] Add Option['Project_factors']... ... to allow projection of factor-values at knots to extrapolation-grid cells --- R/make_data.R | 8 +- inst/executables/VAST_v8_0_0.cpp | 138 ++++++++++--------------------- 2 files changed, 46 insertions(+), 100 deletions(-) diff --git a/R/make_data.R b/R/make_data.R index 4d127a6..3241518 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -75,13 +75,13 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), X_xj=NULL, X_xtp=NULL, Xconfig_zcp=NULL, X_gtp=NULL, X_itp=NULL, Q_ik=NULL, Network_sz=NULL, F_ct=NULL, F_init=1, t_yz=NULL, CheckForErrors=TRUE, yearbounds_zz=NULL, - Options=c('SD_site_logdensity'=0,'Calculate_Range'=0,'Calculate_effective_area'=0,'Calculate_Cov_SE'=0,'Calculate_Synchrony'=0,'Calculate_proportion'=0), + Options=c('SD_site_logdensity'=FALSE,'Calculate_Range'=FALSE,'Calculate_effective_area'=FALSE,'Calculate_Cov_SE'=FALSE), Expansion_cz=NULL ){ # Specify default values for `Options` - Options2use = c('SD_site_density'=0, 'SD_site_logdensity'=0, 'Calculate_Range'=0, 'SD_observation_density'=0, 'Calculate_effective_area'=0, - 'Calculate_Cov_SE'=0, 'Calculate_Synchrony'=0, 'Calculate_Coherence'=0, 'Calculate_proportion'=0, 'normalize_GMRF_in_CPP'=TRUE, - 'Calculate_Fratio'=0, 'Estimate_B0'=0 ) + Options2use = c('SD_site_density'=FALSE, 'SD_site_logdensity'=FALSE, 'Calculate_Range'=FALSE, 'SD_observation_density'=FALSE, 'Calculate_effective_area'=FALSE, + 'Calculate_Cov_SE'=FALSE, 'Calculate_Synchrony'=FALSE, 'Calculate_Coherence'=FALSE, 'Calculate_proportion'=FALSE, 'normalize_GMRF_in_CPP'=TRUE, + 'Calculate_Fratio'=FALSE, 'Estimate_B0'=FALSE, 'Project_factors'=FALSE ) # Replace defaults for `Options` with provided values (if any) for( i in 1:length(Options)){ diff --git a/inst/executables/VAST_v8_0_0.cpp b/inst/executables/VAST_v8_0_0.cpp index cb167cc..0d29db3 100644 --- a/inst/executables/VAST_v8_0_0.cpp +++ b/inst/executables/VAST_v8_0_0.cpp @@ -149,6 +149,25 @@ matrix convert_upper_cov_to_cor( matrix cov ){ return cov; } +// Input: n_g, n_f, n_t, logical_flag, Ags_ij, Ags_x, Mat_sc +template // +array project_knots( int n_g, int n_f, int n_t, int is_epsilon, array Mat_sft, matrix A_ij, vector A_x ){ + array Mat_gf(n_g, n_f); + array Mat_gft(n_g, n_f, n_t); + if( is_epsilon!=1 ) Mat_gf.setZero(); + if( is_epsilon==1 ) Mat_gft.setZero(); + for( int t=0; t // @@ -466,6 +485,7 @@ Type objective_function::operator() () // Slot 9: Include normalization in GMRF PDF // Slot 10: Calculate Fratio as F_ct divided by F achieving 40% of B0 // Slot 11: Calculate B0 and Bratio + // Slot 12: Calculate Omegainput1_gf, Omegainput2_gf, Epsiloninput1_gft, Epsiloninput1_gft // Options_list.yearbounds_zz // Two columns, and 1+ rows, specifying first and last t for each period used in calculating synchrony // Options_list.Expansion_cz @@ -806,19 +826,7 @@ Type objective_function::operator() () Omega1_iz(i,zc) += Ais_x(Arow) * Omega1_sc(s,c_iz(i,zc)); } }} - //for( i=0; i=0) & (c_iz(i,zc) Epsilonmean1_sf(n_s, n_f1 ); @@ -880,24 +888,7 @@ Type objective_function::operator() () Epsilon1_izz(i,zc,zt) = Ais_x(Arow) * Epsilon1_sct(s,c_iz(i,zc),t_iz(i,zt)); }} }}} - //for( i=0; i=0) & (c_iz(i,zc)=0) & (t_iz(i,zt) Ximean1_sc(n_s, 1); @@ -930,23 +921,7 @@ Type objective_function::operator() () Xi1_izp(i,zc,p) += Ais_x(Arow) * Xi1_scp(s,c_iz(i,zc),p); } }}} - //for( i=0; i=0) & (c_iz(i,zc)::operator() () Omega2_iz(i,zc) += Ais_x(Arow) * Omega2_sc(s,c_iz(i,zc)); } }} - //for( i=0; i=0) & (c_iz(i,zc) Epsilonmean2_sf(n_s, n_f2); @@ -1046,24 +1009,7 @@ Type objective_function::operator() () Epsilon2_izz(i,zc,zt) = Ais_x(Arow) * Epsilon2_sct(s,c_iz(i,zc),t_iz(i,zt)); }} }}} - //for( i=0; i=0) & (c_iz(i,zc)=0) & (t_iz(i,zt) Ximean2_sc(n_s, 1); @@ -1096,23 +1042,7 @@ Type objective_function::operator() () Xi2_izp(i,zc,p) += Ais_x(Arow) * Xi2_scp(s,c_iz(i,zc),p); } }}} - //for( i=0; i=0) & (c_iz(i,zc)::operator() () D_i = R1_i * R2_i; ADREPORT( D_i ); } + // Calculate value of vactors at extrapolation-grid cells (e.g., for use when visualizing estimated or rotated factor estimates) + if( Options(12)==1 ){ + array Omegainput1_gf( n_g, Omegainput1_sf.cols() ); + array Epsiloninput1_gft( n_g, Epsiloninput1_sft.col(0).cols(), n_t ); + array Omegainput2_gf( n_g, Omegainput2_sf.cols() ); + array Epsiloninput2_gft( n_g, Epsiloninput2_sft.col(0).cols(), n_t ); + Omegainput1_gf = project_knots( n_g, Omegainput1_sf.cols(), int(1), int(0), Omegainput1_sf, Ags_ij, Ags_x ); + Epsiloninput1_gft = project_knots( n_g, Epsiloninput1_sft.col(0).cols(), n_t, int(1), Epsiloninput1_sft, Ags_ij, Ags_x ); + Omegainput2_gf = project_knots( n_g, Omegainput2_sf.cols(), int(1), int(0), Omegainput2_sf, Ags_ij, Ags_x ); + Epsiloninput2_gft = project_knots( n_g, Epsiloninput2_sft.col(0).cols(), n_t, int(1), Epsiloninput2_sft, Ags_ij, Ags_x ); + REPORT( Omegainput1_gf ); + REPORT( Epsiloninput1_gft ); + REPORT( Omegainput2_gf ); + REPORT( Epsiloninput2_gft ); + } + return jnll; } From c7438124dc3317480cee0b036a2910060820c1a1 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Sun, 10 Mar 2019 20:51:58 -0700 Subject: [PATCH 11/31] Improve numerical stability... ... for lognormal PDF calculation --- inst/executables/VAST_v8_0_0.cpp | 21 +++++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/inst/executables/VAST_v8_0_0.cpp b/inst/executables/VAST_v8_0_0.cpp index 0d29db3..c5ed6d2 100644 --- a/inst/executables/VAST_v8_0_0.cpp +++ b/inst/executables/VAST_v8_0_0.cpp @@ -1171,6 +1171,7 @@ Type objective_function::operator() () vector var_i(n_i); Type tmp_calc1; Type tmp_calc2; + Type log_tmp_calc2; // Linear predictor (pre-link) for presence/absence component matrix P1_iz(n_i,c_iz.cols()); // Response predictor (post-link) @@ -1187,6 +1188,7 @@ Type objective_function::operator() () // ObsModel_ez(e,0) = 4 (ZANB): expected value ("mu") of neg-bin PRIOR to truncating Pr[D=0] -> E[D] = mu/(1-NB(0|mu,var))*phi ALSO Pr[D] = NB(D|mu,var)/(1-NB(0|mu,var))*phi // ObsModel_ez(e,0) = 5 (ZINB): expected value of data for non-zero-inflation component -> E[D] = mu*phi vector R2_i(n_i); + vector log_R2_i(n_i); vector LogProb2_i(n_i); vector maxJ_i(n_i); vector diag_z(4); @@ -1220,6 +1222,10 @@ Type objective_function::operator() () // P2_i: Log-Positive density prediction; R2_i: Positive density prediction R1_i(i) = invlogit( P1_iz(i,0) ); R2_i(i) = a_i(i) * exp( P2_iz(i,0) ); + // Calulate in logspace to prevent numerical over/under-flow + log_R1_i(i) = log(Type(1.0)) - logspace_add( log(Type(1.0)), -1.0*P1_iz(i,0) ); + log_one_minus_R1_i(i) = log(Type(1.0)) - logspace_add( log(Type(1.0)), P1_iz(i,0) ); + log_R2_i(i) = log(a_i(i)) + P2_iz(i,0); } if( (ObsModel_ez(c_iz(i,0),1)==1) | (ObsModel_ez(c_iz(i,0),1)==4) ){ // Poisson-process link, where area-swept affects numbers density exp(P1_i(i)) @@ -1227,17 +1233,21 @@ Type objective_function::operator() () // P2_i: Log-average weight; R2_i: Positive density prediction tmp_calc1 = 0; tmp_calc2 = 0; + log_tmp_calc2 = 0; for( int zc=0; zc=0) & (c_iz(i,zc)=1 ) log_tmp_calc2 = logspace_add( log_tmp_calc2, P1_iz(i,zc) + P2_iz(i,zc) ); } } R1_i(i) = Type(1.0) - exp( -1*a_i(i)*tmp_calc1 ); R2_i(i) = a_i(i) * tmp_calc2 / R1_i(i); // Calulate in logspace to prevent numerical over/under-flow - log_one_minus_R1_i(i) = -1*a_i(i)*tmp_calc1; log_R1_i(i) = logspace_sub( log(Type(1.0)), -1*a_i(i)*tmp_calc1 ); + log_one_minus_R1_i(i) = -1*a_i(i)*tmp_calc1; + log_R2_i(i) = log(a_i(i)) + log_tmp_calc2 - log_R1_i(i); } if( ObsModel_ez(c_iz(i,0),1)==2 ){ // Tweedie link, where area-swept affects numbers density exp(P1_i(i)) @@ -1245,11 +1255,14 @@ Type objective_function::operator() () // P2_i: Log-average weight; R2_i: Expected average weight R1_i(i) = a_i(i) * exp( P1_iz(i,0) ); R2_i(i) = exp( P2_iz(i,0) ); + // Calulate in logspace to prevent numerical over/under-flow + log_R1_i(i) = log(a_i(i)) + P1_iz(i,0); + log_R2_i(i) = P2_iz(i,0); } // Likelihood for delta-models with continuous positive support if( (ObsModel_ez(e_i(i),0)==0) | (ObsModel_ez(e_i(i),0)==1) | (ObsModel_ez(e_i(i),0)==2) ){ // Presence-absence likelihood - if( ObsModel_ez(e_i(i),1)==1 ){ + if( (ObsModel_ez(e_i(i),1)==0) | (ObsModel_ez(e_i(i),1)==1) | (ObsModel_ez(e_i(i),1)==3) | (ObsModel_ez(e_i(i),1)==4) ){ if( b_i(i) > 0 ){ LogProb1_i(i) = log_R1_i(i); }else{ @@ -1276,10 +1289,10 @@ Type objective_function::operator() () } } if(ObsModel_ez(e_i(i),0)==1){ - LogProb2_i(i) = dlnorm(b_i(i), log(R2_i(i))-pow(SigmaM(e_i(i),0),2)/2, SigmaM(e_i(i),0), true); // log-space + LogProb2_i(i) = dlnorm(b_i(i), log_R2_i(i)-pow(SigmaM(e_i(i),0),2)/2, SigmaM(e_i(i),0), true); // log-space // Simulate new values when using obj.simulate() SIMULATE{ - b_i(i) = exp(rnorm( log(R2_i(i))-pow(SigmaM(e_i(i),0),2)/2, SigmaM(e_i(i),0) )); + b_i(i) = exp(rnorm( log_R2_i(i)-pow(SigmaM(e_i(i),0),2)/2, SigmaM(e_i(i),0) )); } } if(ObsModel_ez(e_i(i),0)==2){ From 313713ef12da0085adb8cb669752eec8796f4a25 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Tue, 12 Mar 2019 09:57:05 -0700 Subject: [PATCH 12/31] Update Plot_factors... ... to work with `fine_scale=TRUE` option starting in V8.0.0 --- R/Plot_factors.R | 32 +++++++++++++++++++++----------- 1 file changed, 21 insertions(+), 11 deletions(-) diff --git a/R/Plot_factors.R b/R/Plot_factors.R index c51e875..97ca3dd 100644 --- a/R/Plot_factors.R +++ b/R/Plot_factors.R @@ -15,6 +15,7 @@ #' @export Plot_factors = function( Report, ParHat, Data, SD, Year_Set=NULL, category_names=NULL, RotationMethod="PCA", mapdetails_list=NULL, Dim_year=NULL, Dim_species=NULL, plotdir=paste0(getwd(),"/"), land_color="grey" ){ + # Extract Options and Options_vec (depends upon version) if( all(c("Options","Options_vec") %in% names(TmbData)) ){ Options_vec = TmbData$Options_vec @@ -25,6 +26,8 @@ Plot_factors = function( Report, ParHat, Data, SD, Year_Set=NULL, category_names Options = TmbData$Options_list$Options } + + # Adds intercept defaults to FieldConfig if missing if( is.vector(Data[["FieldConfig"]]) && length(Data[["FieldConfig"]])==4 ){ Data[["FieldConfig"]] = rbind( matrix(Data[["FieldConfig"]],ncol=2,dimnames=list(c("Omega","Epsilon"),c("Component_1","Component_2"))), "Beta"=c("Beta1"=-2,"Beta2"=-2) ) @@ -54,18 +57,18 @@ Plot_factors = function( Report, ParHat, Data, SD, Year_Set=NULL, category_names #Cov_List = Summarize_Covariance( Report=Report, ParHat=ParHat, Data=Data, SD=SD, category_names=category_names, figname=NULL ) # Extract loadings matrices (more numerically stable than extracting covariances, and then re-creating Cholesky) - Psiprime_list = Lprime_list = L_list = vector("list", length=4) # Add names at end so that NULL doesn't interfere + Psi2prime_list = Psiprime_list = Lprime_list = L_list = vector("list", length=6) # Add names at end so that NULL doesn't interfere # Loop through for(i in 1:6){ # Variable names Par_name = c("Omega1", "Epsilon1", "Beta1", "Omega2", "Epsilon2", "Beta2")[i] - if(Par_name == "Omega1") Var_name = "Omegainput1_sf" - if(Par_name == "Epsilon1") Var_name = "Epsiloninput1_sft" - if(Par_name == "Beta1") Var_name = "beta1_ft" - if(Par_name == "Omega2") Var_name = "Omegainput2_sf" - if(Par_name == "Epsilon2") Var_name = "Epsiloninput2_sft" - if(Par_name == "Beta2") Var_name = "beta2_ft" + if(Par_name == "Omega1"){ Var_name = "Omegainput1_sf"; Var2_name = "Omegainput1_gf" } + if(Par_name == "Epsilon1"){ Var_name = "Epsiloninput1_sft"; Var2_name = "Epsiloninput1_gft" } + if(Par_name == "Beta1"){ Var_name = "beta1_ft"; Var2_name = "missing" } + if(Par_name == "Omega2"){ Var_name = "Omegainput2_sf"; Var2_name = "Omegainput2_gf" } + if(Par_name == "Epsilon2"){ Var_name = "Epsiloninput2_sft"; Var2_name = "Epsiloninput2_gft" } + if(Par_name == "Beta2"){ Var_name = "beta2_ft"; Var2_name = "missing" } # Continue if component is included if( as.vector(Data[["FieldConfig"]])[i] > 0 ){ @@ -75,6 +78,7 @@ Plot_factors = function( Report, ParHat, Data, SD, Year_Set=NULL, category_names # Get covariance # SpatialDFA:: Psi_sjt = ParHat[[Var_name]] + Psi_gjt = Report[[Var2_name]] ## the betas are transposed compared to others so fix that here if(Var_name %in% c("beta1_ft", "beta2_ft")){ Psi_sjt <- t(Psi_sjt) @@ -83,7 +87,7 @@ Plot_factors = function( Report, ParHat, Data, SD, Year_Set=NULL, category_names stop(paste("Covariance is empty for parameter", Var_name)) } tau = NULL - logkappa = unlist(ParHat[c('logkappa1','logkappa2')])[c(1,1,1,2,2,3)[i]] + logkappa = unlist(ParHat[c('logkappa1','logkappa2')])[c(1,1,1,2,2,2)[i]] if(Options_vec[8]==0) tau = 1 / (exp(logkappa) * sqrt(4*pi)); if(Options_vec[8]==1) tau = 1 / sqrt(1-exp(logkappa*2)); if( is.null(tau)) stop("Check 'Options_vec[8]' for allowable entries") @@ -92,6 +96,12 @@ Plot_factors = function( Report, ParHat, Data, SD, Year_Set=NULL, category_names rownames(Lprime_list[[i]]) = category_names Psiprime_list[[i]] = Var_rot$Psi_rot + # Extract projected factors is available + if( !is.null(Psi_gjt) ){ + Var2_rot = FishStatsUtils::Rotate_Fn( L_pj=L_list[[i]], Psi=Psi_gjt/tau, RotationMethod=RotationMethod, testcutoff=1e-4 ) + Psi2prime_list[[i]] = Var2_rot$Psi_rot + } + # Plot loadings Dim_factor = Dim( as.vector(Data[["FieldConfig"]])[i] ) png( file=paste0(plotdir,"Factor_loadings--",Par_name,".png"), width=Dim_factor[2]*4, height=Dim_factor[1]*4, units="in", res=200 ) @@ -114,12 +124,12 @@ Plot_factors = function( Report, ParHat, Data, SD, Year_Set=NULL, category_names } } }else{ - Psiprime_list[[i]] = Lprime_list[[i]] = L_list[[i]] = "Element not estimated, and therefore empty" + Psi2prime_list[[i]] = Psiprime_list[[i]] = Lprime_list[[i]] = L_list[[i]] = "Element not estimated, and therefore empty" } } # Return stuff invisibly - names(Psiprime_list) = names(Lprime_list) = names(L_list) = c("Omega1", "Epsilon1", "Beta1", "Omega2", "Epsilon2", "Beta2") - Return = list("Loadings"=L_list, "Rotated_loadings"=Lprime_list, "Rotated_factors"=Psiprime_list) + names(Psi2prime_list) = names(Psiprime_list) = names(Lprime_list) = names(L_list) = c("Omega1", "Epsilon1", "Beta1", "Omega2", "Epsilon2", "Beta2") + Return = list("Loadings"=L_list, "Rotated_loadings"=Lprime_list, "Rotated_factors"=Psiprime_list, "Rotated_projected_factors"=Psi2prime_list) return( invisible(Return) ) } From 13c6c5fa761f2d7f7ddd534b0f051b1b058cd66b Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Wed, 20 Mar 2019 12:47:28 +0000 Subject: [PATCH 13/31] Update single-species example for V8.0.0 --- examples/VAST--single-species_example.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/VAST--single-species_example.Rmd b/examples/VAST--single-species_example.Rmd index c9728e7..94a45ac 100644 --- a/examples/VAST--single-species_example.Rmd +++ b/examples/VAST--single-species_example.Rmd @@ -277,7 +277,7 @@ if( Region == "Other" ){ And we finally generate the information used for conducting spatio-temporal parameter estimation, bundled in list `Spatial_List` ```{r spatial_information, message=FALSE, warning=FALSE, tidy=TRUE, linewidth=60} -Spatial_List = make_spatial_info( grid_size_km=grid_size_km, n_x=n_x, Method=Method, Lon=Data_Geostat[,'Lon'], Lat=Data_Geostat[,'Lat'], Extrapolation_List=Extrapolation_List, DirPath=DateFile, Save_Results=FALSE ) +Spatial_List = make_spatial_info( grid_size_km=grid_size_km, n_x=n_x, Method=Method, Lon_i=Data_Geostat[,'Lon'], Lat_i=Data_Geostat[,'Lat'], Extrapolation_List=Extrapolation_List, DirPath=DateFile, Save_Results=FALSE, fine_scale=TRUE ) # Add knots to Data_Geostat Data_Geostat = cbind( Data_Geostat, "knot_i"=Spatial_List$knot_i ) ``` @@ -288,7 +288,7 @@ Data_Geostat = cbind( Data_Geostat, "knot_i"=Spatial_List$knot_i ) To estimate parameters, we first build a list of data-inputs used for parameter estimation. `make_data` has some simple checks for buggy inputs, but also please read the help file `?make_data`. ```{r build_data, message=FALSE, tidy=TRUE, linewidth=60} -TmbData = make_data("Version"=Version, "FieldConfig"=FieldConfig, "OverdispersionConfig"=OverdispersionConfig, "RhoConfig"=RhoConfig, "ObsModel"=ObsModel, "c_i"=rep(0,nrow(Data_Geostat)), "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(Data_Geostat[,'Vessel'])-1, "s_i"=Data_Geostat[,'knot_i']-1, "t_i"=Data_Geostat[,'Year'], "a_xl"=Spatial_List$a_xl, "MeshList"=Spatial_List$MeshList, "GridList"=Spatial_List$GridList, "Method"=Spatial_List$Method, "Options"=Options ) +TmbData = make_data("Version"=Version, "FieldConfig"=FieldConfig, "OverdispersionConfig"=OverdispersionConfig, "RhoConfig"=RhoConfig, "ObsModel"=ObsModel, "c_i"=rep(0,nrow(Data_Geostat)), "b_i"=Data_Geostat[,'Catch_KG'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=as.numeric(Data_Geostat[,'Vessel'])-1, "s_i"=Data_Geostat[,'knot_i']-1, "t_i"=Data_Geostat[,'Year'], "spatial_list"=Spatial_List, "Options"=Options ) ``` We then build the TMB object. @@ -356,7 +356,7 @@ Finally, we visualize residuals on a map. To do so, we first define years to pl useful plots by first determining which years to plot (`Years2Include`), and labels for each plotted year (`Year_Set`) ```{r plot_years} # Get region-specific settings for plots -MapDetails_List = make_map_info( "Region"=Region, "NN_Extrap"=Spatial_List$PolygonList$NN_Extrap, "Extrapolation_List"=Extrapolation_List ) +MapDetails_List = make_map_info( "Region"=Region, "spatial_list"=Spatial_List, "Extrapolation_List"=Extrapolation_List ) # Decide which years to plot Year_Set = seq(min(Data_Geostat[,'Year']),max(Data_Geostat[,'Year'])) Years2Include = which( Year_Set %in% sort(unique(Data_Geostat[,'Year']))) From 3cee6de63b89c26026582139d0fc6334ae4ab174 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Mon, 25 Mar 2019 20:08:25 +0000 Subject: [PATCH 14/31] small improvement to documentation --- R/VAST.R | 3 ++- man/VAST.Rd | 5 ++++- man/make_data.Rd | 7 +++---- 3 files changed, 9 insertions(+), 6 deletions(-) diff --git a/R/VAST.R b/R/VAST.R index 8460459..ad24121 100644 --- a/R/VAST.R +++ b/R/VAST.R @@ -2,8 +2,9 @@ #' #' VAST is multivariate generalization of package `SpatialDeltaGLMM`, and can analyze data for multiple species, ages, size-classes, etc. #' -#' Please see examples folder on github \url{http://github.com/james-thorson/VAST/tree/master/examples} for up-to-date examples +#' See \code{?fit_model} for a simple of example of high-level wrapper functions for using VAST. Also see examples folder on github \url{http://github.com/james-thorson/VAST/tree/master/examples} for up-to-date examples using lower-level functions. #' +#' @seealso \code{?VAST} for general documentation, \code{?make_settings} for generic settings, \code{?fit_model} for model fitting, and \code{?plot_results} for generic plots #' @docType package #' @name VAST NULL diff --git a/man/VAST.Rd b/man/VAST.Rd index 1525a10..da84768 100644 --- a/man/VAST.Rd +++ b/man/VAST.Rd @@ -9,5 +9,8 @@ VAST is multivariate generalization of package `SpatialDeltaGLMM`, and can analyze data for multiple species, ages, size-classes, etc. } \details{ -Please see examples folder on github \url{http://github.com/james-thorson/VAST/tree/master/examples} for up-to-date examples +See \code{?fit_model} for a simple of example of high-level wrapper functions for using VAST. Also see examples folder on github \url{http://github.com/james-thorson/VAST/tree/master/examples} for up-to-date examples using lower-level functions. +} +\seealso{ +\code{?VAST} for general documentation, \code{?make_settings} for generic settings, \code{?fit_model} for model fitting, and \code{?plot_results} for generic plots } diff --git a/man/make_data.Rd b/man/make_data.Rd index 4fa782c..7db1a48 100644 --- a/man/make_data.Rd +++ b/man/make_data.Rd @@ -15,10 +15,9 @@ make_data(b_i, a_i, c_iz, t_iz, e_i = c_iz[, 1], v_i = rep(0, length(b_i)), X_xj = NULL, X_xtp = NULL, Xconfig_zcp = NULL, X_gtp = NULL, X_itp = NULL, Q_ik = NULL, Network_sz = NULL, F_ct = NULL, F_init = 1, t_yz = NULL, CheckForErrors = TRUE, - yearbounds_zz = NULL, Options = c(SD_site_logdensity = 0, - Calculate_Range = 0, Calculate_effective_area = 0, Calculate_Cov_SE = 0, - Calculate_Synchrony = 0, Calculate_proportion = 0), - Expansion_cz = NULL) + yearbounds_zz = NULL, Options = c(SD_site_logdensity = FALSE, + Calculate_Range = FALSE, Calculate_effective_area = FALSE, + Calculate_Cov_SE = FALSE), Expansion_cz = NULL) } \arguments{ \item{b_i}{Sampled biomass for each observation i} From b570064e9cd7091e1cf3644a37c5bfc0214a40ab Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Fri, 29 Mar 2019 11:27:26 -0700 Subject: [PATCH 15/31] Update User Manual... ... to representing combined-data models and factor-models for intercepts --- manual/VAST_model_structure.docx | Bin 76206 -> 86480 bytes manual/VAST_model_structure.pdf | Bin 275607 -> 662485 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/manual/VAST_model_structure.docx b/manual/VAST_model_structure.docx index e36d444f25744a4bd092d354989b8828614ccbb6..91e1922349d28cfdb0c604ebe5b5baa9817fd4d3 100644 GIT binary patch delta 80868 zcma(1LzE_5(?$)Zd8cjLwr$(CjmkT1+h(P0+qRvRHY!p5yk8Ig9(1qOqd4o#V#Oq` zy`yd!Vxa^QTUibq0v!Yj1O@~IgapKlTzz7d-Q3^to&%%p+&V5>6L~~M(`Xo^ zrX3EGPo^NP2t?^GFN^)%ry`eFvHT~iCWPvqy+~7R#>%Kr#ZGQ?-S1~7iM)Y2dfveF 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z@O*>#T<4&*o!5%vkLN>aCXI0@Tcz~%T#?H5{`^zoxZahIZO5|ocaU`~7sbh~-n!~p zY-KuwtxVy0vsr<7^c-JEwdgstDibRX{i$sCvbAEmW4&#^cKZ~ll?K}CP1UrrOV@!F z;3#?5>Wxe7$zy**Nz%VKbHBa+`1x_uKd`(#@0ZPgeyQwnYwZ1Ye_RKNF_eQppI%-* rfBkYkJ@1Gt=hJ_&?)BU4q From 44aab09bcd0a9296f9cb4e5696d40ba5aa270119 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Sat, 30 Mar 2019 07:05:08 -0700 Subject: [PATCH 16/31] Fix bug in V8.0.0... .. where spatio-temporal variation wasn't being projected properly for when `fine_scale==TRUE`, but was when `fine_scale==FALSE` (hence passing backwards compatibility checks) --- inst/executables/VAST_v8_0_0.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/inst/executables/VAST_v8_0_0.cpp b/inst/executables/VAST_v8_0_0.cpp index c5ed6d2..43671d3 100644 --- a/inst/executables/VAST_v8_0_0.cpp +++ b/inst/executables/VAST_v8_0_0.cpp @@ -885,7 +885,7 @@ Type objective_function::operator() () s = Ais_ij(Arow,1); if( (c_iz(i,zc)>=0) & (c_iz(i,zc)=0) & (t_iz(i,zt)::operator() () s = Ais_ij(Arow,1); if( (c_iz(i,zc)>=0) & (c_iz(i,zc)=0) & (t_iz(i,zt) Date: Mon, 1 Apr 2019 07:22:51 -0700 Subject: [PATCH 17/31] update User Manual h/t Arnaud Gruss --- manual/VAST_model_structure.docx | Bin 86480 -> 82782 bytes manual/VAST_model_structure.pdf | Bin 662485 -> 662812 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/manual/VAST_model_structure.docx b/manual/VAST_model_structure.docx index 91e1922349d28cfdb0c604ebe5b5baa9817fd4d3..15b56b2bab8eb4edba2b4f873e7e8cca3fffbe30 100644 GIT binary patch delta 70359 zcmZ6S1B|9IyRgT$XUDef+2M|D+cw{^ZQHhO+qP}*+3(ALl9NAa+9yqu=1GyeSFg?$ zg71WYW64W_f}sIH06_r(0TBU>47Wa40s{fnK-FUrfdHCPWaI_}kwR|Ce?m=bL+ITZ zN8~x*9F$Sn?mMnz4Gw)x+<@L+WvN)j<_qRvp|7SM`Ljc0Ll51o8o-B&x@p!3ZyVvV zo+LXh`gL<|JyU6=D6>#TcqrFkP;g>pk|#G!QuOc6n${f$P^i-`bfXjNvA$E(XaejK~4=iiFvZ z!Ag3Ho|!>e+^cLBCBoc=AR}(%$`k^S)*txhtO_(^_@YBz2IUb9KFZo2T&+v#e`D77 z&nFe7%s*%}!*qotS)HsNPKIEvTYfruk9hN2YXK|v(fS01h!*yBvY^%OND9I%g9K*# 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z$XrtI*aSoncgSET;Ter<%;;T`G|KEJhU=e9_v#kz%m{MBuG>n;kk&QT2 zs~Uk2e`LJ)l_x!4L*FcOpTh~bo^Jd( zpW(fEWoAx*3qVE&BAQ%exC#Lp$D$hXL-23WNgqk#tD){*y737xXtF1bo*6KztC`-9CZ%p!|0Q1c8z74etm80`o!s zhYtQPm*NE>-yYr}4IMW!ME{pR~y|Nm`wJ{a&X_u+$qf&Zhfe`yQM3xnJh z5X=Yq%OqeZ-(RK!L-}F&uR{EhKJ?y40Eg#9H96a<3Z9br%~2=b?M{|qlsFyudm z{_mjyh5g+i48Y5Gx5qF(2=85O!T6wmne(?b|Jjj0;ROZ(g8vzFelvza`0hF(G6cZx z22>yb1iI@BKmZsy;A;$RKk!1Ob7_AoCAD_{|bI1i^RBi^QOJU4s_@ z??0WK4+usEivJwyNDK`8t3e2u?+ylrfd2~OUTyyI)X zF}{CTV_S5D);- zOM^>`iae=$o#W8z{9iA$|6eY}|CP(c=#|UA&e;$!FF*1QQ;!yx^}qOd{eR)Z^X!$G n8{@y4|8rlV=383 Date: Mon, 1 Apr 2019 12:32:30 -0700 Subject: [PATCH 18/31] adding bounds for new names... ... for beta_rho1_f and beta_rho2_f, h/t Cole Monnahan --- R/make_model.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/R/make_model.R b/R/make_model.R index 766b20f..c537eba 100644 --- a/R/make_model.R +++ b/R/make_model.R @@ -172,6 +172,8 @@ function( TmbData, Version, RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilo Bounds = boundsifpresent_fn( par=Obj$par, name="lambda2", lower=-20, upper=20, bounds=Bounds) Bounds = boundsifpresent_fn( par=Obj$par, name="Beta_rho1", lower=-0.99, upper=0.99, bounds=Bounds) Bounds = boundsifpresent_fn( par=Obj$par, name="Beta_rho2", lower=-0.99, upper=0.99, bounds=Bounds) + Bounds = boundsifpresent_fn( par=Obj$par, name="Beta_rho1_f", lower=-0.99, upper=0.99, bounds=Bounds) + Bounds = boundsifpresent_fn( par=Obj$par, name="Beta_rho2_f", lower=-0.99, upper=0.99, bounds=Bounds) Bounds = boundsifpresent_fn( par=Obj$par, name="Epsilon_rho1", lower=-0.99, upper=0.99, bounds=Bounds) Bounds = boundsifpresent_fn( par=Obj$par, name="Epsilon_rho2", lower=-0.99, upper=0.99, bounds=Bounds) Bounds = boundsifpresent_fn( par=Obj$par, name="Epsilon_rho1_f", lower=-0.99, upper=0.99, bounds=Bounds) From 532d0b523072fd7429dfae52cdd2e6f17fd20e4f Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Fri, 12 Apr 2019 07:27:42 -0800 Subject: [PATCH 19/31] small improvement make_data docs --- R/make_data.R | 18 +++++++++--------- man/make_data.Rd | 18 +++++++++--------- 2 files changed, 18 insertions(+), 18 deletions(-) diff --git a/R/make_data.R b/R/make_data.R index 3241518..390f25a 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -34,7 +34,7 @@ #' \item{ObsModel_ez[e,2]=3}{Conventional delta-model, but fixing encounter probability=1 for any year where all samples encounter the species} #' \item{ObsModel_ez[e,2]=4}{Poisson-link delta-model, but fixing encounter probability=1 for any year where all samples encounter the species and encounter probability=0 for any year where no samples encounter the species} #' } -#' @param RhoConfig OPTIONAL, vector of form c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0) specifying whether either intercepts (Beta1 and Beta2) or spatio-temporal variation (Epsilon1 and Epsilon2) is structured among time intervals (0: each year as fixed effect; 1: each year as random following IID distribution; 2: each year as random following a random walk; 3: constant among years as fixed effect; 4: each year as random following AR1 process) +#' @param RhoConfig vector of form c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0) specifying whether either intercepts (Beta1 and Beta2) or spatio-temporal variation (Epsilon1 and Epsilon2) is structured among time intervals (0: each year as fixed effect; 1: each year as random following IID distribution; 2: each year as random following a random walk; 3: constant among years as fixed effect; 4: each year as random following AR1 process); If missing, assumed to be zero for each element #' @param VamConfig Options to estimate interactions, containing three slots: #' \describe{ #' \item{VamConfig[0]}{selects method for forming interaction matrix; Turn off feature using 0, or I recommend using 2 by default} @@ -47,7 +47,7 @@ #' @param GridList, tagged list representing location information for the 2D AR1 grid hyperdistribution, i.e., from \code{SpatialDeltaGLMM::Spatial_Information_Fn} #' @param Method, character (either "Mesh" or "Grid") specifying hyperdistribution (Default="Mesh") #' @param PredTF_i OPTIONAL, whether each observation i is included in the likelihood (PredTF_i[i]=0) or in the predictive probability (PredTF_i[i]=1) -#' @param X_xtp OPTIONAL, array of dynamic (varying among time intervals) density covariates +#' @param X_xtp array of dynamic (varying among time intervals) density covariates; if missing, assumed to not include covariates #' @param Xconfig_zcp OPTIONAL, 3D array of settings for each dynamic density covariate, where the first dimension corresponds to 1st or 2nd linear predictors, second dimension corresponds to model category, and third dimension corresponds to each density covariate #' \describe{ #' \item{Xconfig_zcp[z,c,p]=0}{\code{X_xtp[,,p]} has no effect on linear predictor z for category c} @@ -55,13 +55,13 @@ #' \item{Xconfig_zcp[z,c,p]=2}{\code{X_xtp[,,p]} has a spatially varying, zero-centered linear effect on linear predictor z for category c} #' \item{Xconfig_zcp[z,c,p]=3}{\code{X_xtp[,,p]} has a spatially varying linear effect on linear predictor z for category c} #' } -#' @param Q_ik OPTIONAL, matrix of catchability covariates (e.g., measured variables affecting catch rates but not caused by variation in species density) for each observation i -#' @param Aniso OPTIONAL, whether to assume isotropy (Aniso=0) or geometric anisotropy (Aniso=1) -#' @param F_ct OPTIONAL, matrix of fishing mortality for each category c and year t (only feasible when using a Poisson-link delta model and specifying temporal structure on intercepts, and mainly interpretable when species interactions via VamConfig) -#' @param t_yz OPTIONAL, matrix specifying combination of levels of \code{t_iz} to use when calculating different indices of abundance or range shifts -#' @param Options OPTIONAL, a vector of form c('SD_site_logdensity'=0,'Calculate_Range'=0,'Calculate_effective_area'=0,'Calculate_Cov_SE'=0,'Calculate_Synchrony'=0,'Calculate_proportion'=0), where Calculate_Range=1 turns on calculation of center of gravity, and Calculate_effective_area=1 turns on calculation of effective area occupied -#' @param yearbounds_zz OPTIONAL, matrix with two columns, giving first and last years for defining one or more periods (rows) used to calculate changes in synchrony over time (only used if \code{Options['Calculate_Synchrony']=1}) -#' @param CheckForErrors OPTIONAL, whether to check for errors in input (NOTE: when CheckForErrors=TRUE, the function will throw an error if it detects a problem with inputs. However, failing to throw an error is no guaruntee that the inputs are all correct) +#' @param Q_ik matrix of catchability covariates (e.g., measured variables affecting catch rates but not caused by variation in species density) for each observation i +#' @param Aniso whether to assume isotropy (Aniso=0) or geometric anisotropy (Aniso=1) +#' @param F_ct matrix of fishing mortality for each category c and year t (only feasible when using a Poisson-link delta model and specifying temporal structure on intercepts, and mainly interpretable when species interactions via VamConfig) +#' @param t_yz matrix specifying combination of levels of \code{t_iz} to use when calculating different indices of abundance or range shifts +#' @param Options a vector of form c('SD_site_logdensity'=0,'Calculate_Range'=0,'Calculate_effective_area'=0,'Calculate_Cov_SE'=0,'Calculate_Synchrony'=0,'Calculate_proportion'=0), where Calculate_Range=1 turns on calculation of center of gravity, and Calculate_effective_area=1 turns on calculation of effective area occupied +#' @param yearbounds_zz matrix with two columns, giving first and last years for defining one or more periods (rows) used to calculate changes in synchrony over time (only used if \code{Options['Calculate_Synchrony']=1}) +#' @param CheckForErrors whether to check for errors in input (NOTE: when CheckForErrors=TRUE, the function will throw an error if it detects a problem with inputs. However, failing to throw an error is no guaruntee that the inputs are all correct) #' @return Tagged list containing inputs to function \code{VAST::Build_TMB_Fn()} diff --git a/man/make_data.Rd b/man/make_data.Rd index 7db1a48..8de572e 100644 --- a/man/make_data.Rd +++ b/man/make_data.Rd @@ -60,7 +60,7 @@ make_data(b_i, a_i, c_iz, t_iz, e_i = c_iz[, 1], v_i = rep(0, \item{ObsModel_ez[e,2]=4}{Poisson-link delta-model, but fixing encounter probability=1 for any year where all samples encounter the species and encounter probability=0 for any year where no samples encounter the species} }} -\item{RhoConfig}{OPTIONAL, vector of form c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0) specifying whether either intercepts (Beta1 and Beta2) or spatio-temporal variation (Epsilon1 and Epsilon2) is structured among time intervals (0: each year as fixed effect; 1: each year as random following IID distribution; 2: each year as random following a random walk; 3: constant among years as fixed effect; 4: each year as random following AR1 process)} +\item{RhoConfig}{vector of form c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0) specifying whether either intercepts (Beta1 and Beta2) or spatio-temporal variation (Epsilon1 and Epsilon2) is structured among time intervals (0: each year as fixed effect; 1: each year as random following IID distribution; 2: each year as random following a random walk; 3: constant among years as fixed effect; 4: each year as random following AR1 process); If missing, assumed to be zero for each element} \item{VamConfig}{Options to estimate interactions, containing three slots: \describe{ @@ -81,11 +81,11 @@ make_data(b_i, a_i, c_iz, t_iz, e_i = c_iz[, 1], v_i = rep(0, \item{s_i}{Spatial knot (e.g., grid cell) for each observation i} -\item{Aniso}{OPTIONAL, whether to assume isotropy (Aniso=0) or geometric anisotropy (Aniso=1)} +\item{Aniso}{whether to assume isotropy (Aniso=0) or geometric anisotropy (Aniso=1)} \item{PredTF_i}{OPTIONAL, whether each observation i is included in the likelihood (PredTF_i[i]=0) or in the predictive probability (PredTF_i[i]=1)} -\item{X_xtp}{OPTIONAL, array of dynamic (varying among time intervals) density covariates} +\item{X_xtp}{array of dynamic (varying among time intervals) density covariates; if missing, assumed to not include covariates} \item{Xconfig_zcp}{OPTIONAL, 3D array of settings for each dynamic density covariate, where the first dimension corresponds to 1st or 2nd linear predictors, second dimension corresponds to model category, and third dimension corresponds to each density covariate \describe{ @@ -95,17 +95,17 @@ make_data(b_i, a_i, c_iz, t_iz, e_i = c_iz[, 1], v_i = rep(0, \item{Xconfig_zcp[z,c,p]=3}{\code{X_xtp[,,p]} has a spatially varying linear effect on linear predictor z for category c} }} -\item{Q_ik}{OPTIONAL, matrix of catchability covariates (e.g., measured variables affecting catch rates but not caused by variation in species density) for each observation i} +\item{Q_ik}{matrix of catchability covariates (e.g., measured variables affecting catch rates but not caused by variation in species density) for each observation i} -\item{F_ct}{OPTIONAL, matrix of fishing mortality for each category c and year t (only feasible when using a Poisson-link delta model and specifying temporal structure on intercepts, and mainly interpretable when species interactions via VamConfig)} +\item{F_ct}{matrix of fishing mortality for each category c and year t (only feasible when using a Poisson-link delta model and specifying temporal structure on intercepts, and mainly interpretable when species interactions via VamConfig)} -\item{t_yz}{OPTIONAL, matrix specifying combination of levels of \code{t_iz} to use when calculating different indices of abundance or range shifts} +\item{t_yz}{matrix specifying combination of levels of \code{t_iz} to use when calculating different indices of abundance or range shifts} -\item{CheckForErrors}{OPTIONAL, whether to check for errors in input (NOTE: when CheckForErrors=TRUE, the function will throw an error if it detects a problem with inputs. However, failing to throw an error is no guaruntee that the inputs are all correct)} +\item{CheckForErrors}{whether to check for errors in input (NOTE: when CheckForErrors=TRUE, the function will throw an error if it detects a problem with inputs. However, failing to throw an error is no guaruntee that the inputs are all correct)} -\item{yearbounds_zz}{OPTIONAL, matrix with two columns, giving first and last years for defining one or more periods (rows) used to calculate changes in synchrony over time (only used if \code{Options['Calculate_Synchrony']=1})} +\item{yearbounds_zz}{matrix with two columns, giving first and last years for defining one or more periods (rows) used to calculate changes in synchrony over time (only used if \code{Options['Calculate_Synchrony']=1})} -\item{Options}{OPTIONAL, a vector of form c('SD_site_logdensity'=0,'Calculate_Range'=0,'Calculate_effective_area'=0,'Calculate_Cov_SE'=0,'Calculate_Synchrony'=0,'Calculate_proportion'=0), where Calculate_Range=1 turns on calculation of center of gravity, and Calculate_effective_area=1 turns on calculation of effective area occupied} +\item{Options}{a vector of form c('SD_site_logdensity'=0,'Calculate_Range'=0,'Calculate_effective_area'=0,'Calculate_Cov_SE'=0,'Calculate_Synchrony'=0,'Calculate_proportion'=0), where Calculate_Range=1 turns on calculation of center of gravity, and Calculate_effective_area=1 turns on calculation of effective area occupied} } \value{ Tagged list containing inputs to function \code{VAST::Build_TMB_Fn()} From 70fe9cfe59a83b1d27d731b75067f6f9b04728c2 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Tue, 16 Apr 2019 09:15:14 -0700 Subject: [PATCH 20/31] small changes to Doxygen docs --- R/make_data.R | 8 ++++---- R/make_model.R | 1 + man/make_data.Rd | 31 ++++++++++++++++--------------- man/make_model.Rd | 2 ++ 4 files changed, 23 insertions(+), 19 deletions(-) diff --git a/R/make_data.R b/R/make_data.R index 390f25a..55c178b 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -10,7 +10,7 @@ #' @param t_iz Matrix where each row species the time for each observation i (if t_iz is a vector, it is coerced to a matrix with one column; if it is a matrix with two or more columns, it specifies multiple times for each observation, e.g., both year and season) #' @param e_i Error distribution for each observation i (by default \code{e_i=c_i}) #' @param v_i OPTIONAL, sampling category (e.g., vessel or tow) associated with overdispersed variation for each observation i -#' @param Version a version number (see example for current default). +#' @param Version a version number; If missing, defaults to latest version using \code{FishStatsUtils::get_latest_version(package="VAST")} #' @param FieldConfig a vector of format c("Omega1"=0, "Epsilon1"=10, "Omega2"="AR1", "Epsilon2"=10), where Omega refers to spatial variation, Epsilon refers to spatio-temporal variation, Omega1 refers to variation in encounter probability, and Omega2 refers to variation in positive catch rates, where 0 is off, "AR1" is an AR1 process, and >0 is the number of elements in a factor-analysis covariance #' @param OverdispersionConfig OPTIONAL, a vector of format c("eta1"=0, "eta2"="AR1") governing any correlated overdispersion among categories for each level of v_i, where eta1 is for encounter probability, and eta2 is for positive catch rates, where 0 is off, "AR1" is an AR1 process, and >0 is the number of elements in a factor-analysis covariance #' @param ObsModel_ez an optional matrix with two columns where first column specifies the distribution for positive catch rates, and second element specifies the functional form for encounter probabilities @@ -68,7 +68,7 @@ #' @export make_data <- function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), - Version, FieldConfig, OverdispersionConfig=c("eta1"=0,"eta2"=0), ObsModel_ez=c("PosDist"=1,"Link"=0), + FieldConfig, OverdispersionConfig=c("eta1"=0,"eta2"=0), ObsModel_ez=c("PosDist"=1,"Link"=0), RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0), VamConfig=c("Method"=0,"Rank"=0,"Timing"=0), spatial_list=NULL, a_xl=NULL, MeshList=spatial_list$MeshList, GridList=spatial_list$GridList, Method=spatial_list$Method, s_i=spatial_list$knot_i-1, Aniso=TRUE, PredTF_i=rep(0,length(b_i)), @@ -76,7 +76,7 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), Q_ik=NULL, Network_sz=NULL, F_ct=NULL, F_init=1, t_yz=NULL, CheckForErrors=TRUE, yearbounds_zz=NULL, Options=c('SD_site_logdensity'=FALSE,'Calculate_Range'=FALSE,'Calculate_effective_area'=FALSE,'Calculate_Cov_SE'=FALSE), - Expansion_cz=NULL ){ + Expansion_cz=NULL, Version=FishStatsUtils::get_latest_version(package="VAST") ){ # Specify default values for `Options` Options2use = c('SD_site_density'=FALSE, 'SD_site_logdensity'=FALSE, 'Calculate_Range'=FALSE, 'SD_observation_density'=FALSE, 'Calculate_effective_area'=FALSE, @@ -84,7 +84,7 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), 'Calculate_Fratio'=FALSE, 'Estimate_B0'=FALSE, 'Project_factors'=FALSE ) # Replace defaults for `Options` with provided values (if any) - for( i in 1:length(Options)){ + for( i in seq_along(Options) ){ if(tolower(names(Options)[i]) %in% tolower(names(Options2use))){ Options2use[[match(tolower(names(Options)[i]),tolower(names(Options2use)))]] = Options[[i]] } diff --git a/R/make_model.R b/R/make_model.R index c537eba..3def380 100644 --- a/R/make_model.R +++ b/R/make_model.R @@ -14,6 +14,7 @@ #' @param DiagnosticDir OPTIONAL, a directory where diagonstic runtime information should be stored #' @param TmbDir OPTIONAL, a directory where the CPP file for the VAST model can be found locally #' @param RunDir OPTIONAL, a directory where the CPP file is copied, copiled, and run (must have write privileges or else the function will crash) +#' @param build_model Boolean indicating whether to build the model, \code{build_model=TRUE}, or simply build the inputs, \code{build_model=FALSE} #' @inheritParams Data_Fn #' @return Tagged list containing objects for running a VAST model diff --git a/man/make_data.Rd b/man/make_data.Rd index 8de572e..5b743eb 100644 --- a/man/make_data.Rd +++ b/man/make_data.Rd @@ -5,19 +5,20 @@ \title{Build data input for VAST model} \usage{ make_data(b_i, a_i, c_iz, t_iz, e_i = c_iz[, 1], v_i = rep(0, - length(b_i)), Version, FieldConfig, OverdispersionConfig = c(eta1 = 0, - eta2 = 0), ObsModel_ez = c(PosDist = 1, Link = 0), - RhoConfig = c(Beta1 = 0, Beta2 = 0, Epsilon1 = 0, Epsilon2 = 0), - VamConfig = c(Method = 0, Rank = 0, Timing = 0), spatial_list = NULL, - a_xl = NULL, MeshList = spatial_list$MeshList, - GridList = spatial_list$GridList, Method = spatial_list$Method, - s_i = spatial_list$knot_i - 1, Aniso = TRUE, PredTF_i = rep(0, - length(b_i)), X_xj = NULL, X_xtp = NULL, Xconfig_zcp = NULL, - X_gtp = NULL, X_itp = NULL, Q_ik = NULL, Network_sz = NULL, - F_ct = NULL, F_init = 1, t_yz = NULL, CheckForErrors = TRUE, - yearbounds_zz = NULL, Options = c(SD_site_logdensity = FALSE, - Calculate_Range = FALSE, Calculate_effective_area = FALSE, - Calculate_Cov_SE = FALSE), Expansion_cz = NULL) + length(b_i)), FieldConfig, OverdispersionConfig = c(eta1 = 0, eta2 = + 0), ObsModel_ez = c(PosDist = 1, Link = 0), RhoConfig = c(Beta1 = 0, + Beta2 = 0, Epsilon1 = 0, Epsilon2 = 0), VamConfig = c(Method = 0, Rank + = 0, Timing = 0), spatial_list = NULL, a_xl = NULL, + MeshList = spatial_list$MeshList, GridList = spatial_list$GridList, + Method = spatial_list$Method, s_i = spatial_list$knot_i - 1, + Aniso = TRUE, PredTF_i = rep(0, length(b_i)), X_xj = NULL, + X_xtp = NULL, Xconfig_zcp = NULL, X_gtp = NULL, X_itp = NULL, + Q_ik = NULL, Network_sz = NULL, F_ct = NULL, F_init = 1, + t_yz = NULL, CheckForErrors = TRUE, yearbounds_zz = NULL, + Options = c(SD_site_logdensity = FALSE, Calculate_Range = FALSE, + Calculate_effective_area = FALSE, Calculate_Cov_SE = FALSE), + Expansion_cz = NULL, + Version = FishStatsUtils::get_latest_version(package = "VAST")) } \arguments{ \item{b_i}{Sampled biomass for each observation i} @@ -32,8 +33,6 @@ make_data(b_i, a_i, c_iz, t_iz, e_i = c_iz[, 1], v_i = rep(0, \item{v_i}{OPTIONAL, sampling category (e.g., vessel or tow) associated with overdispersed variation for each observation i} -\item{Version}{a version number (see example for current default).} - \item{FieldConfig}{a vector of format c("Omega1"=0, "Epsilon1"=10, "Omega2"="AR1", "Epsilon2"=10), where Omega refers to spatial variation, Epsilon refers to spatio-temporal variation, Omega1 refers to variation in encounter probability, and Omega2 refers to variation in positive catch rates, where 0 is off, "AR1" is an AR1 process, and >0 is the number of elements in a factor-analysis covariance} \item{OverdispersionConfig}{OPTIONAL, a vector of format c("eta1"=0, "eta2"="AR1") governing any correlated overdispersion among categories for each level of v_i, where eta1 is for encounter probability, and eta2 is for positive catch rates, where 0 is off, "AR1" is an AR1 process, and >0 is the number of elements in a factor-analysis covariance} @@ -106,6 +105,8 @@ make_data(b_i, a_i, c_iz, t_iz, e_i = c_iz[, 1], v_i = rep(0, \item{yearbounds_zz}{matrix with two columns, giving first and last years for defining one or more periods (rows) used to calculate changes in synchrony over time (only used if \code{Options['Calculate_Synchrony']=1})} \item{Options}{a vector of form c('SD_site_logdensity'=0,'Calculate_Range'=0,'Calculate_effective_area'=0,'Calculate_Cov_SE'=0,'Calculate_Synchrony'=0,'Calculate_proportion'=0), where Calculate_Range=1 turns on calculation of center of gravity, and Calculate_effective_area=1 turns on calculation of effective area occupied} + +\item{Version}{a version number; If missing, defaults to latest version using \code{FishStatsUtils::get_latest_version(package="VAST")}} } \value{ Tagged list containing inputs to function \code{VAST::Build_TMB_Fn()} diff --git a/man/make_model.Rd b/man/make_model.Rd index f1f38ae..2965e26 100644 --- a/man/make_model.Rd +++ b/man/make_model.Rd @@ -33,6 +33,8 @@ make_model(TmbData, Version, RhoConfig = c(Beta1 = 0, Beta2 = 0, Epsilon1 \item{TmbDir}{OPTIONAL, a directory where the CPP file for the VAST model can be found locally} \item{RunDir}{OPTIONAL, a directory where the CPP file is copied, copiled, and run (must have write privileges or else the function will crash)} + +\item{build_model}{Boolean indicating whether to build the model, \code{build_model=TRUE}, or simply build the inputs, \code{build_model=FALSE}} } \value{ Tagged list containing objects for running a VAST model From 315b7f58e8d2a06575d908d7b4ca6239dd437574 Mon Sep 17 00:00:00 2001 From: "James.Thorson" <1454196437@mil> Date: Tue, 16 Apr 2019 14:56:43 -0700 Subject: [PATCH 21/31] Fix bug in make_data... ... which was triggered when using more than one covariate with fine_scale=TRUE in V8.0.0 --- R/make_data.R | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/R/make_data.R b/R/make_data.R index 55c178b..69c6319 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -102,7 +102,9 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), warning("Please consider using input `a_gl`; `a_xl` is still available for backwards compatiblity but only `a_gl` will be used") } if( FishStatsUtils::convert_version_name(Version) >= FishStatsUtils::convert_version_name("VAST_v8_0_0") ){ - if( !is.null(X_xtp) ) stop("`X_xtp` is not used in version >= 8.0.0") + if( !is.null(X_xtp) ){ + stop("`X_xtp` is not used in version >= 8.0.0") + } } if( FishStatsUtils::convert_version_name(Version) <= FishStatsUtils::convert_version_name("VAST_v7_0_0") ){ if( !is.null(X_gtp) | !is.null(X_itp) ) stop("`X_gtp` and `X_itp` are not used in version <= 7.0.0") @@ -195,7 +197,11 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), for( cI in seq(2,ncol(tprime_iz),length=ncol(tprime_iz)-1)) t_yz = cbind(t_yz, min(tprime_iz[,cI],na.rm=TRUE)) } n_j = ncol(X_xj) - n_p = dim(X_xtp)[3] + if( FishStatsUtils::convert_version_name(Version) >= FishStatsUtils::convert_version_name("VAST_v8_0_0") ){ + n_p = dim(X_gtp)[3] + }else{ + n_p = dim(X_xtp)[3] + } n_k = ncol(Q_ik) n_y = nrow(t_yz) @@ -268,7 +274,7 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), if( CheckForErrors==TRUE ){ if( ncol(ObsModel_ez)!=2 | nrow(ObsModel_ez)!=n_e ) stop("Wrong dimensions for ObsModel_ez") if( !is.matrix(a_gl) | !is.matrix(X_xj) | !is.matrix(Q_ik) ) stop("a_gl, X_xj, and Q_ik should be matrices") - if( !is.array(X_xtp) | !is.array(X_gtp) | !is.array(X_itp) ) stop( "X_xtp, X_gtp, and X_itp should be matrices") + if( !is.array(X_xtp) | !is.array(X_gtp) | !is.array(X_itp) ) stop( "X_xtp, X_gtp, and X_itp should be arrays") if( (max(s_i)-1)>n_x | min(s_i)<0 ) stop("s_i exceeds bounds in MeshList") if( any(a_i<=0) ) stop("a_i must be greater than zero for all observations, and at least one value of a_i is not") # Warnings about all positive or zero @@ -314,7 +320,12 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), if( nrow(a_gl)!=n_x | ncol(a_gl)!=n_l ) stop("a_xl has wrong dimensions") if( nrow(X_xj)!=n_x | ncol(X_xj)!=n_j ) stop("X_xj has wrong dimensions") if( nrow(Q_ik)!=n_i | ncol(Q_ik)!=n_k ) stop("Q_ik has wrong dimensions") - if( dim(X_xtp)[1]!=n_x | dim(X_xtp)[2]!=n_t | dim(X_xtp)[3]!=n_p ) stop("X_xtp has wrong dimensions") + if( FishStatsUtils::convert_version_name(Version) >= FishStatsUtils::convert_version_name("VAST_v8_0_0") ){ + if( dim(X_gtp)[1]!=n_g | dim(X_gtp)[2]!=n_t | dim(X_gtp)[3]!=n_p ) stop("X_gtp has wrong dimensions") + if( dim(X_itp)[1]!=n_i | dim(X_itp)[2]!=n_t | dim(X_itp)[3]!=n_p ) stop("X_itp has wrong dimensions") + }else{ + if( dim(X_xtp)[1]!=n_x | dim(X_xtp)[2]!=n_t | dim(X_xtp)[3]!=n_p ) stop("X_xtp has wrong dimensions") + } if( ncol(c_iz)>1 & any(ObsModel_ez[,2]!=1) ) stop("Using multiple columnns in `c_iz` only makes sense using a Poisson-link delta model via `ObsModel[2]=1`") if( nrow(F_ct)!=n_c | ncol(F_ct)!=n_t ) stop("F_ct has wrong dimensions") } From 00a59fdaebac4249f5d09fa0f9b0acf55147d087 Mon Sep 17 00:00:00 2001 From: "James.Thorson" <1454196437@mil> Date: Thu, 18 Apr 2019 11:37:08 -0700 Subject: [PATCH 22/31] Push "treat_encounter_as_zero"... ... up the stack as an Option input to make_data. This is used e.g. in comp-expansion --- R/Param_Fn.R | 15 +++++++++------ R/make_data.R | 23 ++++++++++++++++++----- tests/testthat/test-comp-expansion.R | 9 +++------ 3 files changed, 30 insertions(+), 17 deletions(-) diff --git a/R/Param_Fn.R b/R/Param_Fn.R index c6495c0..86585f9 100644 --- a/R/Param_Fn.R +++ b/R/Param_Fn.R @@ -175,7 +175,8 @@ function( Version, DataList, RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsil Params_tmp = list( "beta1_ct"=NA, "beta2_ct"=NA ) # Starting values if( all(DataList$ObsModel_ez[,2] %in% c(0,3)) ){ - Params_tmp[["beta1_ct"]] = qlogis(0.01*0.99*tapply(ifelse(DataList$b_i>0,1,0),INDEX=factor(DataList$c_iz[,1],levels=sort(unique(DataList$c_iz[,1]))),FUN=mean)) %o% rep(1,DataList$n_t) + Prop_c = tapply( ifelse(DataList$b_i>0,1,0), INDEX=factor(DataList$c_iz[,1],levels=sort(unique(DataList$c_iz[,1]))), FUN=mean, na.rm=TRUE) + Params_tmp[["beta1_ct"]] = qlogis(0.01*0.99*Prop_c) %o% rep(1,DataList$n_t) Params_tmp[["beta2_ct"]] = log(tapply(ifelse(DataList$b_i>0,DataList$b_i/DataList$a_i,NA),INDEX=factor(DataList$c_iz[,1],levels=sort(unique(DataList$c_iz[,1]))),FUN=mean,na.rm=TRUE)) %o% rep(1,DataList$n_t) } if( all(DataList$ObsModel_ez[,2] %in% c(1,2,4)) ){ @@ -184,8 +185,8 @@ function( Version, DataList, RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsil } # Over-ride starting values for 100% and 0% encounters if( all(DataList$ObsModel_ez[,2] %in% c(3)) ){ - Tmp_ct = tapply(ifelse(DataList$b_i>0,1,0), INDEX=list(factor(DataList$c_iz[,1],levels=sort(unique(DataList$c_iz[,1]))),factor(DataList$t_iz[,1],levels=1:DataList$n_t-1)), FUN=mean) - if( any(is.na(Tmp_ct) | Tmp_ct==1) ) Params_tmp[["beta1_ct"]][which(is.na(Tmp_ct) | Tmp_ct==1)] = 20 + Prop_ct = tapply(ifelse(DataList$b_i>0,1,0), INDEX=list(factor(DataList$c_iz[,1],levels=sort(unique(DataList$c_iz[,1]))),factor(DataList$t_iz[,1],levels=1:DataList$n_t-1)), FUN=mean) + if( any(is.na(Prop_ct) | Prop_ct==1) ) Params_tmp[["beta1_ct"]][which(is.na(Prop_ct) | Prop_ct==1)] = 20 } if( all(DataList$ObsModel_ez[,2] %in% c(4)) ){ Tmp_ct = tapply(ifelse(DataList$b_i>0,1,0), INDEX=list(factor(DataList$c_iz[,1],levels=sort(unique(DataList$c_iz[,1]))),factor(DataList$t_iz[,1],levels=1:DataList$n_t-1)), FUN=mean) @@ -194,10 +195,10 @@ function( Version, DataList, RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsil if( any(is.na(Tmp_ct) | Tmp_ct==0) ) Params_tmp[["beta2_ct"]][which(is.na(Tmp_ct) | Tmp_ct==0)] = 0 } # Deal with any potential problems - if(length(Params_tmp[["beta1_ct"]])==1 && is.na(Params_tmp[["beta1_ct"]])){ + if( any(is.na(Params_tmp[["beta1_ct"]])) ){ Params_tmp[["beta1_ct"]] = array(0, dim=c(DataList$n_c,DataList$n_t)) } - if(length(Params_tmp[["beta2_ct"]])==1 && is.na(Params_tmp[["beta2_ct"]])){ + if( any(is.na(Params_tmp[["beta2_ct"]])) ){ Params_tmp[["beta2_ct"]] = array(0, dim=c(DataList$n_c,DataList$n_t)) } # Insert with name appropriate for a given version @@ -285,7 +286,9 @@ function( Version, DataList, RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsil } # Error messages - if( any(sapply(Return, FUN=function(num){any(is.na(num))})) ) stop("Some parameter is NA") + if( any(sapply(Return, FUN=function(num){any(is.na(num))})) ){ + stop("Some parameter is NA") + } # Return tagged list return( Return ) diff --git a/R/make_data.R b/R/make_data.R index 69c6319..3a3b728 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -81,7 +81,7 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), # Specify default values for `Options` Options2use = c('SD_site_density'=FALSE, 'SD_site_logdensity'=FALSE, 'Calculate_Range'=FALSE, 'SD_observation_density'=FALSE, 'Calculate_effective_area'=FALSE, 'Calculate_Cov_SE'=FALSE, 'Calculate_Synchrony'=FALSE, 'Calculate_Coherence'=FALSE, 'Calculate_proportion'=FALSE, 'normalize_GMRF_in_CPP'=TRUE, - 'Calculate_Fratio'=FALSE, 'Estimate_B0'=FALSE, 'Project_factors'=FALSE ) + 'Calculate_Fratio'=FALSE, 'Estimate_B0'=FALSE, 'Project_factors'=FALSE, 'treat_nonencounter_as_zero'=FALSE ) # Replace defaults for `Options` with provided values (if any) for( i in seq_along(Options) ){ @@ -149,6 +149,15 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), # Coerce ObsModel_ez to be a matrix if( !is.matrix(ObsModel_ez) ) ObsModel_ez = matrix( ObsModel_ez, ncol=2, nrow=n_e, byrow=TRUE ) + # if Options2use['treat_nonencounter_as_zero']==TRUE, then replace b_i for all year-categories with all zeros with NAs + if( Options2use['treat_nonencounter_as_zero']==TRUE ){ + # Determine year-category pairs with no data + Index = list( factor(c_iz[,1],levels=0:max(c_iz[,1])), factor(tprime_iz[,1],levels=0:max(tprime_iz[,1])) ) + Num_ct = tapply( b_i, INDEX=Index, FUN=function(vec){sum(vec>0)} ) + Num_ct = ifelse( is.na(Num_ct), 0, Num_ct ) + b_i = ifelse( Num_ct[cbind(as.numeric(Index[[1]]),as.numeric(Index[[2]]))]==0, NA, b_i ) + } + # Covariates and defaults if( is.null(X_xj) ){ X_xj = matrix(0, nrow=n_x, ncol=1) @@ -283,10 +292,14 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), Prop_nonzero = Prop_nonzero[-1,] } if( any(ObsModel_ez[,2] %in% c(0,1)) ){ - if( any(!is.na(Prop_nonzero) & (Prop_nonzero==0|Prop_nonzero==1)) ){ - if( RhoConfig[1]==0 ){ + if( RhoConfig[1]==0 ){ + if( any(!is.na(Prop_nonzero) & (Prop_nonzero==1)) ){ + print( Prop_nonzero ) + stop("Some years and/or categories have 100% encounters, and this requires either temporal structure of a different link-function") + } + if( any(!is.na(Prop_nonzero) & (Prop_nonzero==0)) & Options2use['treat_nonencounter_as_zero']==FALSE ){ print( Prop_nonzero ) - stop("Some years and/or categories have either all or no encounters, and this requires either temporal structure of a different link-function") + stop("Some years and/or categories have 0% encounters, and this requires either temporal structure of a different link-function") } } } @@ -581,7 +594,7 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), # Check for NAs if( CheckForErrors==TRUE ){ - NoNAs = setdiff( names(Return), c("t_iz","t_yz","c_iz","Network_sz") ) + NoNAs = setdiff( names(Return), c("t_iz","t_yz","c_iz","Network_sz","b_i") ) if( any(sapply(Return[NoNAs], FUN=function(Array){any(is.na(Array))})==TRUE) ) stop("Please find and eliminate the NA from your inputs") } diff --git a/tests/testthat/test-comp-expansion.R b/tests/testthat/test-comp-expansion.R index 7dee6b9..afc9262 100644 --- a/tests/testthat/test-comp-expansion.R +++ b/tests/testthat/test-comp-expansion.R @@ -13,15 +13,12 @@ test_that("Male lingcod compositional expansion is working ", { # Run model Extrapolation_List = make_extrapolation_info( Region="California_current", strata.limits=strata.limits ) Spatial_List = make_spatial_info( grid_size_km=grid_size_km, n_x=n_x, Method=Method, Lon=Data_Geostat[,'BEST_LON_DD'], Lat=Data_Geostat[,'BEST_LAT_DD'], Extrapolation_List=Extrapolation_List, DirPath=test_path ) - TmbData = VAST::Data_Fn("CheckForErrors"=FALSE, "Version"=Version_VAST, "OverdispersionConfig"=OverdispersionConfig, "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=ObsModel_Est, "c_i"=as.numeric(Data_Geostat[,'Length_bin'])-1, "b_i"=Data_Geostat[,'First_stage_expanded_numbers'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=rep(0,nrow(Data_Geostat)), "t_i"=Data_Geostat[,'Year'], "spatial_list"=Spatial_List, "Aniso"=Aniso ) - # Switch missing categories to NA - EncProb_tc = tapply( TmbData$b_i, INDEX=list(TmbData$t_i,TmbData$c_i), FUN=function(vec){mean(vec>0)} ) - TmbData$b_i = ifelse( EncProb_tc[1+cbind(TmbData$t_i,TmbData$c_i)]==0, NA, TmbData$b_i ) + TmbData = make_data("Options"=list("treat_nonencounter_as_zero"=TRUE), "Version"=Version_VAST, "OverdispersionConfig"=OverdispersionConfig, "FieldConfig"=FieldConfig, "RhoConfig"=RhoConfig, "ObsModel"=ObsModel_Est, "c_i"=as.numeric(Data_Geostat[,'Length_bin'])-1, "b_i"=Data_Geostat[,'First_stage_expanded_numbers'], "a_i"=Data_Geostat[,'AreaSwept_km2'], "v_i"=rep(0,nrow(Data_Geostat)), "t_i"=Data_Geostat[,'Year'], "spatial_list"=Spatial_List, "Aniso"=Aniso ) # Build object - TmbList = Build_TMB_Fn("build_model"=TRUE, "TmbData"=TmbData, "RunDir"=test_path, "Version"=Version_VAST, "RhoConfig"=RhoConfig, "loc_x"=Spatial_List$loc_x, "Npool"=Npool) + TmbList = make_model("build_model"=TRUE, "TmbData"=TmbData, "RunDir"=test_path, "Version"=Version_VAST, "RhoConfig"=RhoConfig, "loc_x"=Spatial_List$loc_x, "Npool"=Npool) # Optimize on.exit( dyn.unload(paste0(test_path,"/",TMB::dynlib(Version_VAST))), add=TRUE ) - Opt = TMBhelper::Optimize( obj=TmbList[["Obj"]], getsd=TRUE, lower=TmbList[["Lower"]], upper=TmbList[["Upper"]], newtonsteps=1 ) # , rel.tol=1e-20 + Opt = TMBhelper::Optimize( obj=TmbList[["Obj"]], getsd=FALSE, lower=TmbList[["Lower"]], upper=TmbList[["Upper"]], newtonsteps=1 ) # , rel.tol=1e-20 # Comparisons Par1 = Opt$par[names(Opt$par)%in%c("ln_H_input","beta1_ct","beta1_ft","logkappa1","beta2_ct","beta2_ft","logkappa1","logSigmaM")] Par2 = opt$par[names(opt$par)%in%c("ln_H_input","beta1_ct","logkappa1","beta2_ct","logkappa1","logSigmaM")] From aac1828efad0f68ee095292e97f19c9cb6d9bed2 Mon Sep 17 00:00:00 2001 From: "James.Thorson" <1454196437@mil> Date: Mon, 22 Apr 2019 09:08:16 -0700 Subject: [PATCH 23/31] Moving remaining support functinos.... ... to FishStatsUtils --- NAMESPACE | 8 -- R/Coherence.R | 92 ---------------- R/Crossvalidate_Fn.R | 76 -------------- R/Plot_Overdispersion.R | 63 ----------- R/Plot_factors.R | 135 ------------------------ R/Rerun_Fn.R | 124 ---------------------- R/Summarize_Covariance.R | 140 ------------------------- R/calc_coherence.R | 92 ---------------- R/calc_cov.R | 45 -------- R/calc_synchrony.R | 140 ------------------------- R/calculate_proportion.R | 75 ------------- R/plot_eigen.R | 17 --- R/{Summarize.R => summarize_results.R} | 0 R/zzz.R | 44 ++++++++ man/Coherence.Rd | 28 ----- man/Crossvalidate_Fn.Rd | 34 ------ man/Plot_Overdispersion.Rd | 39 ++----- man/Plot_factors.Rd | 37 ++----- man/Rerun_Fn.Rd | 53 ---------- man/Summarize.Rd | 10 +- man/Summarize_Covariance.Rd | 44 ++------ man/calc_coherence.Rd | 28 ----- man/calc_cov.Rd | 20 ---- man/calc_synchrony.Rd | 24 ----- man/calculate_proportion.Rd | 49 --------- tests/testthat.R | 1 + tests/testthat/test-comp-expansion.R | 1 + 27 files changed, 75 insertions(+), 1344 deletions(-) delete mode 100644 R/Coherence.R delete mode 100644 R/Crossvalidate_Fn.R delete mode 100644 R/Plot_Overdispersion.R delete mode 100644 R/Plot_factors.R delete mode 100644 R/Rerun_Fn.R delete mode 100644 R/Summarize_Covariance.R delete mode 100644 R/calc_coherence.R delete mode 100644 R/calc_cov.R delete mode 100644 R/calc_synchrony.R delete mode 100644 R/calculate_proportion.R delete mode 100644 R/plot_eigen.R rename R/{Summarize.R => summarize_results.R} (100%) delete mode 100644 man/Coherence.Rd delete mode 100644 man/Crossvalidate_Fn.Rd delete mode 100644 man/Rerun_Fn.Rd delete mode 100644 man/calc_coherence.Rd delete mode 100644 man/calc_cov.Rd delete mode 100644 man/calc_synchrony.Rd delete mode 100644 man/calculate_proportion.Rd diff --git a/NAMESPACE b/NAMESPACE index 9ba6813..5c730f6 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,23 +1,15 @@ # Generated by roxygen2: do not edit by hand export(Build_TMB_Fn) -export(Coherence) -export(Crossvalidate_Fn) export(Data_Fn) export(Make_Map) export(Param_Fn) export(Plot_Overdispersion) export(Plot_factors) -export(Rerun_Fn) export(Summarize) export(Summarize_Covariance) -export(calc_coherence) -export(calc_cov) -export(calc_synchrony) -export(calculate_proportion) export(make_data) export(make_model) -export(plot_eigen) importFrom(grDevices,colorRampPalette) importFrom(grDevices,dev.off) importFrom(grDevices,png) diff --git a/R/Coherence.R b/R/Coherence.R deleted file mode 100644 index 5ec8669..0000000 --- a/R/Coherence.R +++ /dev/null @@ -1,92 +0,0 @@ - -#' Calculate stability metrics -#' -#' \code{Coherence} calculates buffering (`phi`) and coherence (`psi`) -#' -#' @inheritParams Plot_Overdispersion -#' @inheritParams Data_Fn -#' @param covhat estimated covariance used for calculating coherence - -#' @return Tagged list containing measures of synchrony -#' \describe{ -#' \item{phi_xz}{Synchrony index for each site (x) and each period (row of \code{yearbounds_zz})} -#' \item{phi_z}{weighted-average of \code{phi_xz} for each period, weighted by average community-abundance at each site in that period} -#' \item{psi}{Measure of proportion of variance explained by leading eigen-vectors} -#' \item{L_c}{Cholesky decomposition of \code{covhat}} -#' } - -#' @export -Coherence = function( Report, Data, covhat=NULL, yearbounds_zz=matrix(c(1,Data$n_t),nrow=1) ){ - - ################## - # Synchrony - ################## - n_z = nrow(yearbounds_zz) - - # Category-specific density ("D"), mean and variance - varD_xcz = array( NA, dim=c(Data$n_x,Data$n_c,n_z) ) - - # Community density ("C") summing across categories - C_xt = array(0, dim=c(Data$n_x,Data$n_t)) - varC_xz = array(NA, dim=c(Data$n_x,n_z)) - - # Area-weighted total biomass ("B") for each category - B_ct = array(0, dim=c(Data$n_c,Data$n_t)) - B_t = rep(0, Data$n_t) - - # Proportion of total biomass ("P") for each station - P_xz = array(NA, dim=c(Data$n_x,n_z)) - - # Synchrony indices - phi_xz = array(NA, dim=c(Data$n_x,n_z)) - phi_z = rep(0, n_z) - - # Temporary variables - temp_xt = array(NA, dim=c(Data$n_x,Data$n_t)) - temp_c = rep(NA, Data$n_c) - - # Derived quantities - for( tI in 1:Data$n_t ){ - for( cI in 1:Data$n_c ){ - for( xI in 1:Data$n_x ){ - C_xt[xI,tI] = C_xt[xI,tI] + Report$D_xct[xI,cI,tI] - B_ct[cI,tI] = B_ct[cI,tI] + Report$D_xct[xI,cI,tI] * Data$a_xl[xI,1] - } - B_t[tI] = B_t[tI] + B_ct[cI,tI] - } - } - - # Loop through periods (only using operations available in TMB, i.e., var, mean, row, col, segment) - for( zI in 1:n_z){ - for( xI in 1:Data$n_x){ - # Variance for each category - for( cI in 1:Data$n_c ){ - for( tI in 1:Data$n_t ) temp_xt[xI,tI] = Report$D_xct[xI,cI,tI] - varD_xcz[xI,cI,zI] = var(temp_xt[xI,][yearbounds_zz[zI,1]:yearbounds_zz[zI,2]]) - } - # Variance for combined across categories - varC_xz[xI,zI] = var(C_xt[xI,][yearbounds_zz[zI,1]:yearbounds_zz[zI,2]]) - # Proportion in each category - P_xz[xI,zI] = Data$a_xl[xI,1] * mean( C_xt[xI,][yearbounds_zz[zI,1]:yearbounds_zz[zI,2]] / B_t[yearbounds_zz[zI,1]:yearbounds_zz[zI,2]] ) - # Synchrony index - for( cI in 1:Data$n_c ) temp_c[cI] = varD_xcz[xI,cI,zI] - phi_xz[xI,zI] = varC_xz[xI,zI] / sum(sqrt(temp_c))^2 - phi_z[zI] = phi_z[zI] + (phi_xz[xI,zI] * P_xz[xI,zI]) - }} - - ################## - # Coherence - ################## - - # Coherence index - if( !is.null(covhat) ){ - L_c = eigen(covhat)$values - psi = 2 * (mean(cumsum(L_c)/sum(L_c))-0.5) - }else{ - L_c = psi = NULL - } - - # Return stuff - Return = list("phi_xz"=phi_xz, "phi_z"=phi_z, "psi"=psi, "P_xz"=P_xz, "L_c"=L_c) - return( Return ) -} diff --git a/R/Crossvalidate_Fn.R b/R/Crossvalidate_Fn.R deleted file mode 100644 index 983a00f..0000000 --- a/R/Crossvalidate_Fn.R +++ /dev/null @@ -1,76 +0,0 @@ - -#' K-fold crossvalidation -#' -#' \code{Crossvalidate_Fn} runs a k-fold crossvalidation analysis on a fitted model object -#' -#' @param record_dir, a directory with writing privilege where the runs are stored -#' @param parhat, a tagged list of parameters from the fitted model, e.g., as generated by \code{parhat <- obj$env$parList()} after covergence -#' @param original_data, a tagged list of data for the VAST model -#' @param group_i, a vector of positive integers, indicating the k-fold group for each observation (default=NULL, which generates a new group_i with even probability) -#' @param kfold, the number of crossvalidation batches used (default=10) -#' @param skip_finished boolean specifying whether to rerun (skip_finished==FALSE) or skip (skip_finished==TRUE) previously completed runs (Default=FALSE) -#' @param skip_finished boolean specifying whether to rerun (skip_finished==FALSE) or skip (skip_finished==TRUE) previously completed runs (Default=FALSE) -#' @param newtonsteps number of extra newton steps to take after optimization (alternative to \code{loopnum}) -#' @param ... Additional arguments to pass to \code{VAST::Build_TMB_Fn} - -#' @return Results a matrix with total predictive negative log-likelihood for each crossvalidation partition, and number of crossvalidation samples for that partition - -#' @export -Crossvalidate_Fn = function(record_dir, parhat, original_data, group_i=NULL, kfold=10, newtonsteps=1, skip_finished=FALSE, ... ){ - # Lump observations into groups - if( is.null(group_i) || length(group_i)!=original_data$n_i ){ - message( "Generating group_i" ) - Group_i = sample( x=1:kfold, size=original_data$n_i, replace=TRUE ) - }else{ - message( "Using input group_i" ) - Group_i = group_i - } - save( Group_i, file=paste0(record_dir,"Group_i.RData")) - - # Results - Results = array(NA, dim=c(kfold,3), dimnames=list(paste0("k=",1:kfold),c("predictive_negloglike","number_of_crossvalidation_samples","predictive_nll_per_sample")) ) - - # Loop through - for(i in 1:kfold){ - # Directory - CrossvalidationDir = paste0(record_dir,"k=",i,"/") - dir.create( CrossvalidationDir ) - - # Run - if( "Save.RData"%in%list.files(CrossvalidationDir) & skip_finished==TRUE ){ - load(file=paste0(CrossvalidationDir,"Save.RData")) - }else{ - # Modify data - Data = original_data - Data$PredTF_i = ifelse(Group_i==i,1,0) - - # Build new one - TmbList = VAST::Build_TMB_Fn("TmbData"=Data, "Parameters"=parhat, ...)#, "Random"=NULL) - #TmbList = VAST::Build_TMB_Fn("TmbData"=Data, "Parameters"=parhat, "RunDir"=TmbDir, "Version"=Version, "loc_x"=loc_x, "RhoConfig"=RhoConfig, "TmbDir"=TmbDir, "Use_REML"=Use_REML) #, "Map"=Save$Map, "Random"=NULL) - - # Extract objects - Obj = TmbList[["Obj"]] - TmbList[["Upper"]][grep("logkappa",names(TmbList[["Upper"]]))] = Inf - - # Run model - Opt = TMBhelper::Optimize( obj=Obj, lower=TmbList[["Lower"]], upper=TmbList[["Upper"]], newtonsteps=newtonsteps ) # , rel.tol=1e-20 - - # Reports - Report = Obj$report( Obj$env$last.par.best ) - ParHat = Obj$env$parList(Opt$par) - - # Save stuff - Save = list("Opt"=Opt, "Report"=Report, "ParHat"=ParHat, "TmbData"=Data, "Map"=TmbList$Map) - save(Save, file=paste0(CrossvalidationDir,"Save.RData")) - capture.output( Opt, file=paste0(CrossvalidationDir,"Opt.txt")) - } - - # Load results - Results[i,c("predictive_negloglike","number_of_crossvalidation_samples")] = c( Save$Report$pred_jnll, sum(Save$TmbData$PredTF_i)) - Results[i,"predictive_nll_per_sample"] = Results[i,"predictive_negloglike"] / Results[i,"number_of_crossvalidation_samples"] - } - - # Return output - return( Results) -} - diff --git a/R/Plot_Overdispersion.R b/R/Plot_Overdispersion.R deleted file mode 100644 index 36f6303..0000000 --- a/R/Plot_Overdispersion.R +++ /dev/null @@ -1,63 +0,0 @@ - -#' Plot estimated overdispersion -#' -#' \code{Plot_Overdispersion} plots sample and analytic estimates of overdispersion -#' -#' @param filename1 filename (including absolute path) for overdispersion for component #1 -#' @param filename2 filename (including absolute path) for overdispersion for component #2 -#' @param Data tagged list of input data -#' @param ParHat Tagged list of fitted data, e.g., from \code{ParHat <- obj$env$parList()} -#' @param Report output report, e.g., from \code{Report <- obj$report()} -#' @param SD standard deviation report, e.g., from \code{SD <- sdreport( obj )} -#' @param Map tagged list of map inputs -#' @param ControlList1 plotting size for component #1 -#' @param ControlIist2 plotting size for component #2 (default: ControlList2 <- ControlList1) - -#' @return Tagged list containing inputs to function Build_TMB_Fn() -#' \describe{ -#' \item{Cov1_cc}{Covariance for component #1 (typically probability of occurence)} -#' \item{Cov2_cc}{Covariance for component #2 (typically positive catch rates)} -#' } - -#' @export -Plot_Overdispersion = function( filename1, filename2, Data, ParHat, Report, SD=NULL, Map=NULL, - ControlList1=list("Width"=8, "Height"=4, "Res"=200, "Units"='in'), ControlList2=ControlList1 ){ - - # Loop through components - for(i in 1:2){ - if( Data[["OverdispersionConfig"]][i]>=0 ){ - - if(i==1){ - Cov_cc = VAST:::calc_cov( n_f=Data[["OverdispersionConfig"]][i], n_c=Data[["n_c"]], L_z=ParHat[["L1_z"]]) - cov_eta_vc = cov( Report[["eta1_vc"]] ) - } - if(i==2){ - Cov_cc = VAST:::calc_cov( n_f=Data[["OverdispersionConfig"]][i], n_c=Data[["n_c"]], L_z=ParHat[["L2_z"]]) - cov_eta_vc = cov( Report[["eta2_vc"]] ) - } - - # Plot covariance - png( file=paste0(filename1,"_",i,".png"), width=ControlList1$Width, height=ControlList1$Height, res=ControlList1$Res, units=ControlList1$Units ) - par( mfrow=c(2,1), mar=c(0,2,3,0), mgp=c(2,0.5,0), tck=-0.02, oma=c(0,0,0,0)) - for(j in 1:2){ - Cov_cc = list(Cov_cc, cov_eta_vc)[[j]] - VAST:::plot_cov( Cov=Cov_cc ) - title( main=c("Encounter prob (pop.)","Positive catch rate (pop.)","Encounter prob (sample)","Positive catch rate (sample)")[j*2-2+i], line=2 ) - } - dev.off() - - # Plot distributions - png( file=paste0(filename2,"_",i,".png"), width=ControlList2$Width, height=ControlList2$Height, res=ControlList2$Res, units=ControlList2$Units ) - Mat = list( Report[["eta1_vc"]], Report[["eta2_vc"]] )[[i]] - par( mfrow=rep(Data$n_c,2), mar=c(0,0,0,0), mgp=c(2,0.5,0), tck=-0.02, oma=c(0,0,0,0)) - for(j1 in 1:Data$n_c){ - for(j2 in 1:Data$n_c){ - if(j1==j2) hist( Mat[,j1], xlab="", ylab="", xaxt="n", yaxt="n" ) - if(j1!=j2) plot( x=Mat[,j1], y=Mat[,j2], xlab="", ylab="", xaxt="n", yaxt="n" ) - }} - dev.off() - }else{ - message( "No overdispersion for ", c("presence/absence","positive catch rates")[i], " component so not generating output...") - } - } -} diff --git a/R/Plot_factors.R b/R/Plot_factors.R deleted file mode 100644 index 97ca3dd..0000000 --- a/R/Plot_factors.R +++ /dev/null @@ -1,135 +0,0 @@ - -#' Plot factor-decomposition of covariance -#' -#' \code{Plot_factors} plots factor loadings, average spatial factors, and spatio-temporal factors -#' -#' @inheritParams Plot_Overdispersion -#' @inheritParams Summarize_Covariance -#' @param Year_Set plotting-names for time dimension -#' @param mapdetails_list output from \code{FishStatsUtils::MapDetails_Fn} -#' @param Dim_year Plotting dimension (row,column) for plot of years (default: square with sufficient size for number of years) -#' @param Dim_species Plotting dimension (row,column) for plot of categories (default: square with sufficient size for number of categories) -#' @param plotdir directory for saving plots -#' @param land_color color for filling in land (use \code{land_color=rgb(0,0,0,alpha=0)} for transparent land) - -#' @export -Plot_factors = function( Report, ParHat, Data, SD, Year_Set=NULL, category_names=NULL, RotationMethod="PCA", - mapdetails_list=NULL, Dim_year=NULL, Dim_species=NULL, plotdir=paste0(getwd(),"/"), land_color="grey" ){ - - # Extract Options and Options_vec (depends upon version) - if( all(c("Options","Options_vec") %in% names(TmbData)) ){ - Options_vec = TmbData$Options_vec - Options = TmbData$Options - } - if( "Options_list" %in% names(TmbData) ){ - Options_vec = TmbData$Options_list$Options_vec - Options = TmbData$Options_list$Options - } - - - - # Adds intercept defaults to FieldConfig if missing - if( is.vector(Data[["FieldConfig"]]) && length(Data[["FieldConfig"]])==4 ){ - Data[["FieldConfig"]] = rbind( matrix(Data[["FieldConfig"]],ncol=2,dimnames=list(c("Omega","Epsilon"),c("Component_1","Component_2"))), "Beta"=c("Beta1"=-2,"Beta2"=-2) ) - }else{ - if( !is.matrix(Data[["FieldConfig"]]) || !all(dim(Data[["FieldConfig"]])==c(3,2)) ){ - stop("`FieldConfig` has the wrong dimensions in `Summarize_Covariance`") - } - } - - # Fill in missing inputs - if( "D_xct" %in% names(Report) ){ - if( is.null(Year_Set) ) Year_Set = 1:dim(Report$D_xct)[3] - if( is.null(category_names) ) category_names = 1:dim(Report$D_xct)[2] - } - if( "D_xcy" %in% names(Report) ){ - if( is.null(Year_Set) ) Year_Set = 1:dim(Report$D_xcy)[3] - if( is.null(category_names) ) category_names = 1:dim(Report$D_xcy)[2] - Report[["D_xct"]] = Report[["D_xcy"]] - } - - # Dimensions for plotting - Dim = function( num ) c(ceiling(sqrt(num)), ceiling(num/ceiling(sqrt(num))) ) - Dim_year = Dim(length(Year_Set)) - Dim_species = Dim(length(category_names)) - - # Extract covariance - #Cov_List = Summarize_Covariance( Report=Report, ParHat=ParHat, Data=Data, SD=SD, category_names=category_names, figname=NULL ) - - # Extract loadings matrices (more numerically stable than extracting covariances, and then re-creating Cholesky) - Psi2prime_list = Psiprime_list = Lprime_list = L_list = vector("list", length=6) # Add names at end so that NULL doesn't interfere - - # Loop through - for(i in 1:6){ - # Variable names - Par_name = c("Omega1", "Epsilon1", "Beta1", "Omega2", "Epsilon2", "Beta2")[i] - if(Par_name == "Omega1"){ Var_name = "Omegainput1_sf"; Var2_name = "Omegainput1_gf" } - if(Par_name == "Epsilon1"){ Var_name = "Epsiloninput1_sft"; Var2_name = "Epsiloninput1_gft" } - if(Par_name == "Beta1"){ Var_name = "beta1_ft"; Var2_name = "missing" } - if(Par_name == "Omega2"){ Var_name = "Omegainput2_sf"; Var2_name = "Omegainput2_gf" } - if(Par_name == "Epsilon2"){ Var_name = "Epsiloninput2_sft"; Var2_name = "Epsiloninput2_gft" } - if(Par_name == "Beta2"){ Var_name = "beta2_ft"; Var2_name = "missing" } - - # Continue if component is included - if( as.vector(Data[["FieldConfig"]])[i] > 0 ){ - # Get loadings matrix - L_list[[i]] = calc_cov( L_z=ParHat[[paste0("L_",tolower(Par_name),"_z")]], n_f=as.vector(Data[["FieldConfig"]])[i], n_c=Data$n_c, returntype="loadings_matrix" ) - rownames(L_list[[i]]) = category_names - - # Get covariance # SpatialDFA:: - Psi_sjt = ParHat[[Var_name]] - Psi_gjt = Report[[Var2_name]] - ## the betas are transposed compared to others so fix that here - if(Var_name %in% c("beta1_ft", "beta2_ft")){ - Psi_sjt <- t(Psi_sjt) - } - if(is.null(Psi_sjt)){ - stop(paste("Covariance is empty for parameter", Var_name)) - } - tau = NULL - logkappa = unlist(ParHat[c('logkappa1','logkappa2')])[c(1,1,1,2,2,2)[i]] - if(Options_vec[8]==0) tau = 1 / (exp(logkappa) * sqrt(4*pi)); - if(Options_vec[8]==1) tau = 1 / sqrt(1-exp(logkappa*2)); - if( is.null(tau)) stop("Check 'Options_vec[8]' for allowable entries") - Var_rot = FishStatsUtils::Rotate_Fn( L_pj=L_list[[i]], Psi=Psi_sjt/tau, RotationMethod=RotationMethod, testcutoff=1e-4 ) - Lprime_list[[i]] = Var_rot$L_pj_rot - rownames(Lprime_list[[i]]) = category_names - Psiprime_list[[i]] = Var_rot$Psi_rot - - # Extract projected factors is available - if( !is.null(Psi_gjt) ){ - Var2_rot = FishStatsUtils::Rotate_Fn( L_pj=L_list[[i]], Psi=Psi_gjt/tau, RotationMethod=RotationMethod, testcutoff=1e-4 ) - Psi2prime_list[[i]] = Var2_rot$Psi_rot - } - - # Plot loadings - Dim_factor = Dim( as.vector(Data[["FieldConfig"]])[i] ) - png( file=paste0(plotdir,"Factor_loadings--",Par_name,".png"), width=Dim_factor[2]*4, height=Dim_factor[1]*4, units="in", res=200 ) - par( mfrow=Dim_factor, mar=c(0,2,2,0) ) - for( cI in 1:as.vector(Data[["FieldConfig"]])[i] ) FishStatsUtils::PlotLoadings( L_pj=Var_rot$L_pj_rot, whichfactor=cI ) - dev.off() - - # Plot factors - if( !is.null(mapdetails_list) ){ - # Plot factors by year - if( Par_name %in% c("Epsilon1","Epsilon2")){ - FishStatsUtils::plot_maps(plot_set=c(NA,6,NA,NA,7,NA)[i], MappingDetails=mapdetails_list[["MappingDetails"]], Report=list("D_xct"=Var_rot$Psi_rot,"Epsilon1_sct"=Var_rot$Psi_rot,"Epsilon2_sct"=Var_rot$Psi_rot), PlotDF=mapdetails_list[["PlotDF"]], MapSizeRatio=mapdetails_list[["MapSizeRatio"]], Xlim=mapdetails_list[["Xlim"]], Ylim=mapdetails_list[["Ylim"]], FileName=plotdir, Year_Set=Year_Set, Rotate=mapdetails_list[["Rotate"]], category_names=paste0("Factor_",1:dim(Var_rot$Psi_rot)[2]), mar=c(0,0,2,0), oma=c(1.5,1.5,0,0), pch=20, Cex=mapdetails_list[["Cex"]], cex=1.8, mfrow=Dim_year, cex.main=1.0, Legend=mapdetails_list[["Legend"]], zone=mapdetails_list[["Zone"]], plot_legend_fig=FALSE, land_color=land_color) - } # - - # Plot average factors across years - if( !Par_name %in% c("Beta1", "Beta2")){ - ## Doesn't make sense to make maps of beta factors since they aren't spatial - Mat_sf = apply(Var_rot$Psi_rot, MARGIN=1:2, FUN=mean) - FishStatsUtils::PlotMap_Fn( MappingDetails=mapdetails_list[["MappingDetails"]], Mat=Mat_sf, PlotDF=mapdetails_list[["PlotDF"]], MapSizeRatio=mapdetails_list[["MapSizeRatio"]], Xlim=mapdetails_list[["Xlim"]], Ylim=mapdetails_list[["Ylim"]], FileName=paste0(plotdir,"Factor_maps--",Par_name), Year_Set=paste0("Factor_",1:ncol(Mat_sf)), Rotate=mapdetails_list[["Rotate"]], zone=mapdetails_list[["Zone"]], mar=c(0,0,2,0), oma=c(2.5,2.5,0,0), pch=20, Cex=mapdetails_list[["Cex"]], mfrow=Dim_factor, Legend=mapdetails_list[["Legend"]], plot_legend_fig=FALSE, land_color=land_color) - } - } - }else{ - Psi2prime_list[[i]] = Psiprime_list[[i]] = Lprime_list[[i]] = L_list[[i]] = "Element not estimated, and therefore empty" - } - } - - # Return stuff invisibly - names(Psi2prime_list) = names(Psiprime_list) = names(Lprime_list) = names(L_list) = c("Omega1", "Epsilon1", "Beta1", "Omega2", "Epsilon2", "Beta2") - Return = list("Loadings"=L_list, "Rotated_loadings"=Lprime_list, "Rotated_factors"=Psiprime_list, "Rotated_projected_factors"=Psi2prime_list) - return( invisible(Return) ) -} diff --git a/R/Rerun_Fn.R b/R/Rerun_Fn.R deleted file mode 100644 index f1bd481..0000000 --- a/R/Rerun_Fn.R +++ /dev/null @@ -1,124 +0,0 @@ - -#' Explore counter-factual scenario -#' -#' \code{Rerun_Fn} re-builds a model while fixing a subset of parameters at zero -#' -#' @param parhat0 parameter values to use by default (presumably maximum-likelihood estimates) -#' @param turnoff_pars character-vector of parameters to turn off -#' @param loc_x location for each knot used to calculate center-of-gravity for counter-factual runs with colnames c('E_km','N_km','Lon','Lat') -#' @param cov_to_turnoff which covariates to turn off, as indicated by their order in \code{TmbData$X_xtp} (which only matters if "gamma1_ctp" or "gamma2_ctp" is in \code{turnoff_pars}) -#' @param calculate_COG Boolean whether to calculate COG for each run -#' @param figname name for figure to plot density in counter-factual scenario -#' @inheritParams Build_TMB_Fn -#' @param MapDetails_List output from \code{FishStatsUtils::MapDetails_Fn} -#' @param year_set set of parameters to include -#' @param c_set set of categories to include -#' @param ... additional arguments passed to \code{VAST::Build_TMB_Fn} - -#' @return Tagged list -#' \describe{ -#' \item{Report}{Report output for counter-factual run} -#' \item{NewBuild_List}{Output from \code{VAST::Build_TMB_Fn} using counter-factual parameters} -#' } - -#' @examples -#' \dontrun{ -#' # Run without GMRF -#' -#' Rerun_Fn(parhat0 = Obj$env$parList(), -#' turnoff_pars = c("Epsiloninput1_sft", "Epsiloninput2_sft"), -#' loc_x = Spatial_List$loc_x, -#' TmbData = TmbData, Version = "VAST_v4_0_0") -#' } - -#' @export -Rerun_Fn = function( parhat0, turnoff_pars, loc_x, cov_to_turnoff=1:dim(parhat0[["gamma2_ctp"]])[3], calculate_COG=TRUE, figname=NULL, - Map="generate", MapDetails_List=NULL, year_set=1:ncol(parhat0[["beta1_ct"]]), c_set=1:nrow(parhat0[["beta1_ct"]]), ... ){ - - # Local function -- calculate center of gravity - Calc_COG = function( z_x, B_xt ){ - # Set-up - if( is.vector(z_x) ) z_x = matrix( z_x, ncol=1 ) - P_xt = B_xt / ( rep(1,nrow(B_xt)) %o% apply(B_xt, MARGIN=2, FUN=sum) ) - - # Loop through dimensions - COG_t = NULL - for( i in 1:ncol(z_x)){ - COG_t = cbind( COG_t, apply( P_xt, MARGIN=2, FUN=weighted.mean, x=z_x[,i])) - } - colnames(COG_t) =colnames(z_x) - - return( COG_t ) - } - - # Local function -- plot density maps - plot_density = function( Report, MapDetails_List, type, year_set, c_set ){ - if("D_xct" %in% names(Report)) D_xcy = Report$D_xct - if("D_xcy" %in% names(Report)) D_xcy = Report$D_xcy - if( type=="year" ){ - Dim = c("Nrow"=ceiling(sqrt(length(year_set)))); Dim = c(Dim,"Ncol"=ceiling(length(year_set)/Dim['Nrow'])) - for( i in 1:(length(c_set)-1) ){ - Mat = log(D_xcy[,i,]) - Zlim = range( log(D_xcy[,i,]) ) - FishStatsUtils:::PlotMap_Fn( MappingDetails=MapDetails_List[["MappingDetails"]], Mat=Mat, zlim=Zlim, PlotDF=MapDetails_List[["PlotDF"]], MapSizeRatio=MapDetails_List[["MapSizeRatio"]], Xlim=MapDetails_List[["Xlim"]], Ylim=MapDetails_List[["Ylim"]], FileName="", Year_Set=year_set, Rescale=FALSE, Rotate=MapDetails_List[["Rotate"]], Format="", Res=NA, mfrow=Dim, mar=c(0,0,2,0), oma=c(3.5,3.5,4,0), Cex=MapDetails_List[["Cex"]], textmargin=textmargin, add=FALSE, pch=20, zone=MapDetails_List[["Zone"]] ) - mtext( side=3, outer=TRUE, text=paste0("Size bounds:",c_set[i]," to ",c_set[i+1]), cex=1.5, line=1 ) - } - } - if( type=="category" ){ - Dim = c("Nrow"=ceiling(sqrt(length(c_set)-1))); Dim = c(Dim,"Ncol"=ceiling((length(c_set)-1)/Dim['Nrow'])) - for( i in 1:length(year_set) ){ - Mat = log(D_xcy[,,i]) - Zlim = range( log(D_xcy[,,i]) ) - FishStatsUtils:::PlotMap_Fn( MappingDetails=MapDetails_List[["MappingDetails"]], Mat=Mat, zlim=Zlim, PlotDF=MapDetails_List[["PlotDF"]], MapSizeRatio=MapDetails_List[["MapSizeRatio"]], Xlim=MapDetails_List[["Xlim"]], Ylim=MapDetails_List[["Ylim"]], FileName="", Year_Set=c_set[-1], Rescale=FALSE, Rotate=MapDetails_List[["Rotate"]], Format="", Res=NA, mfrow=Dim, mar=c(0,0,2,0), oma=c(3.5,3.5,4,0), Cex=MapDetails_List[["Cex"]], textmargin=textmargin, add=FALSE, pch=20, zone=MapDetails_List[["Zone"]] ) - mtext( side=3, outer=TRUE, text=paste0("Year:",year_set[i]), cex=1.5, line=1 ) - } - } # - } - - # Check for errors - if( !all(turnoff_pars %in% c("Epsiloninput1_sft","Epsiloninput2_sft","beta1_ct","beta2_ct","gamma1_ctp","gamma2_ctp")) ) stop("Some parameters in turnoff_pars are not as expected") - if( !all(turnoff_pars %in% names(parhat0)) ) stop("Some parameters in turnoff_pars are not in parhat0") - - # Eliminate spatio-temporal variation and differences in age-composition - parhat_mod = parhat0 - if( "Epsiloninput1_sft" %in% turnoff_pars ) parhat_mod[["Epsiloninput1_sft"]][] = 0 - if( "Epsiloninput2_sft" %in% turnoff_pars ) parhat_mod[["Epsiloninput2_sft"]][] = 0 - if( "beta1_ct" %in% turnoff_pars ) parhat_mod[["beta1_ct"]][] = outer( rowMeans(ParHat[["beta1_ct"]]), rep(1,ncol(ParHat[["beta1_ct"]])) ) - if( "beta2_ct" %in% turnoff_pars ) parhat_mod[["beta2_ct"]][] = outer( rowMeans(ParHat[["beta2_ct"]]), rep(1,ncol(ParHat[["beta2_ct"]])) ) - which_cov_to_turnoff = intersect(cov_to_turnoff, 1:dim(parhat_mod[["gamma1_ctp"]])[3]) - if( "gamma1_ctp" %in% turnoff_pars ) parhat_mod[["gamma1_ctp"]][,,which_cov_to_turnoff] = 0 - if( "gamma2_ctp" %in% turnoff_pars ) parhat_mod[["gamma2_ctp"]][,,which_cov_to_turnoff] = 0 - - # Rebuild - NewBuild_List = Build_TMB_Fn( "Parameters"=parhat_mod, Map=Map, ... ) - Obj = NewBuild_List[["Obj"]] - Report = Obj$report( ) - Return = list("Report"=Report, "NewBuild_List"=NewBuild_List) - - # calculate COG - if( calculate_COG==TRUE ){ - # Figure out column names to use - ColNames = c('E_km','N_km') - if( all(c('Lon','Lat') %in% colnames(loc_x)) ) ColNames = c(ColNames,c('Lon','Lat')) - # Total - B_xt = apply(Report$Index_xcyl[,,,1,drop=FALSE], MARGIN=c(1,3), FUN=sum ) - Return[["COG_t"]] = Calc_COG( z_x=loc_x[,ColNames], B_xt=B_xt ) - # By category - Return[["COG_ct"]] = NULL - for( cI in 1:dim(Report$Index_xcyl)[2] ){ - Return[["COG_ct"]] = abind::abind( Return[["COG_ct"]], Calc_COG(z_x=loc_x[,ColNames], B_xt=Report$Index_xcyl[,cI,,1]), along=3) - } - Return[["COG_ct"]] = aperm(Return[["COG_ct"]], c(3,1,2)) - } - - # Plot - #if( !is.null(figname) & !is.null(MapDetails_List) ){ - # message( "Starting plot for counter-factual run" ) - # ThorsonUtilities::save_fig( file=figname, width=8, height=12, type="pdf", onefile=TRUE ) - # plot_density( Report=Report, MapDetails_List=MapDetails_List, type="year", year_set=Year_Set, c_set=c_set ) - # dev.off() - #} - - # Return - return( Return ) -} diff --git a/R/Summarize_Covariance.R b/R/Summarize_Covariance.R deleted file mode 100644 index c6b9eb6..0000000 --- a/R/Summarize_Covariance.R +++ /dev/null @@ -1,140 +0,0 @@ - -#' Explore spatio-temporal covariance -#' -#' \code{Summarize_Covariance} plots and returns spatio-temporal covariance among categories -#' -#' @inheritParams Plot_Overdispersion -#' @param category_order integer-vector to re-order categories while plotting -#' @param category_names character-vector listing name for each category -#' @param plotdir directory (absolute path) for plots -#' @param figname character for first-part of figure names -#' @param plotTF integer-vector (length 4) specifying which covariances to plot (recommended: use \code{plotTF=FieldConfig}) -#' @param plot_cor Boolean, whether to plot correlation or covariance -#' @param mgp passed to \code{par} -#' @param tck passed to \code{par} -#' @param oma passed to \code{par} -#' @param ... passed to \code{ThorsonUtilities::save_fig} - -#' @export -Summarize_Covariance = function( Report, Data, ParHat, SD=NULL, category_order=1:Data$n_c, category_names=1:Data$n_c, - plotdir=paste0(getwd(),"/"), figname="Cov", plotTF=NULL, plot_cor=TRUE, - mgp=c(2,0.5,0), tck=-0.02, oma=c(0,5,2,0), ...){ - - # Adds intercept defaults to FieldConfig if missing - if( is.vector(Data[["FieldConfig"]]) && length(Data[["FieldConfig"]])==4 ){ - Data[["FieldConfig"]] = rbind( matrix(Data[["FieldConfig"]],ncol=2,dimnames=list(c("Omega","Epsilon"),c("Component_1","Component_2"))), "Beta"=c("Beta1"=-2,"Beta2"=-2) ) - }else{ - if( !is.matrix(Data[["FieldConfig"]]) || !all(dim(Data[["FieldConfig"]])==c(3,2)) ){ - stop("`FieldConfig` has the wrong dimensions in `Summarize_Covariance`") - } - } - - # Add default for plotTF, or coerce Data$FieldConfig to a vector - if( is.null(plotTF) ){ - plotTF = as.vector( Data[["FieldConfig"]]>0 ) - }else{ - plotTF = as.vector(plotTF) - } - - # Object to return - Return = list() - - # Extract - for(i in which(Data[["FieldConfig"]]>=0) ){ - Par_name = c("omega1", "epsilon1", "beta1", "omega2", "epsilon2", "beta2")[i] - L_name = paste0("L_",Par_name,"_z") - - # Extract estimates and standard errors - if( !is.null(SD) ){ - # Object to build - sd_summary = summary(SD) - Slot_name = paste0("lowercov_uppercor_",Par_name) - if( Slot_name %in% rownames(sd_summary) ){ - # Extract covariances - Cor = Cov = Mat = ThorsonUtilities::Extract_SE( SD=SD, parname=Slot_name, columns=1:2, Dim=c(Data$n_c,Data$n_c) ) - dimnames(Cor) = dimnames(Cov) = list( category_names, category_names, c("Estimate","Std.Error") ) - # Cor - Cor[,,1][lower.tri(Cor[,,1])] = t(Mat[,,1])[lower.tri(Mat[,,1])] - diag(Cor[,,1]) = 1 - Cor[,,2][lower.tri(Cor[,,2])] = t(Mat[,,2])[lower.tri(Mat[,,2])] - diag(Cor[,,2]) = NA - # Cov - Cov[,,1][upper.tri(Cov[,,1])] = t(Mat[,,1])[upper.tri(Mat[,,1])] - Cov[,,2][upper.tri(Cov[,,2])] = t(Mat[,,2])[upper.tri(Mat[,,2])] - }else{ - Cov = Cor = NULL - } - }else{ - Cov = Cor = NULL - } - - # Extract estimates - if( is.null(Cov) | is.null(Cor) ){ - Cov = Cor = array( NA, dim=c(Data$n_c,Data$n_c,2), dimnames=list(category_names,category_names,c("Estimate","Std.Error") ) ) - Cov[,,'Estimate'] = calc_cov( L_z=ParHat[[L_name]], n_f=as.vector(Data[["FieldConfig"]])[i], n_c=Data$n_c ) - Cor[,,'Estimate'] = cov2cor( Cov[,,'Estimate'] ) - } # - - # Add to return - List = list( Cor, Cov ) - names(List) = paste0(c("Cor_","Cov_"), Par_name) - Return = c( Return, List ) - } - - # Plot covariances - if( !is.null(figname) ){ - # Work out dimensions - Dim = c(3,2) - if( sum(ifelse(plotTF>0,1,0))==1 ) Dim = c(1,1) - if( all(ifelse(plotTF>0,1,0)==c(1,1,0,0,0,0)) | all(ifelse(plotTF>0,1,0)==c(0,0,1,1,0,0)) ) Dim=c(1,2) - if( all(ifelse(plotTF>0,1,0)==c(1,0,1,0,0,0)) | all(ifelse(plotTF>0,1,0)==c(0,1,0,1,0,0)) ) Dim=c(2,1) - - # Conversion function - if(plot_cor==TRUE){ - convert = function(Cov) ifelse(is.na(cov2cor(Cov)),0,cov2cor(Cov)) - }else{ - convert = function(Cov) ifelse(is.na(Cov),0,Cov) - } - - # Plot analytic - ThorsonUtilities::save_fig( file=paste0(plotdir,figname,"--Analytic.png"), width=Dim[2]*4+1, height=Dim[1]*4, ... ) - par(mfrow=Dim, mar=c(0,1,1,0), mgp=mgp, tck=tck, oma=oma) - for(i in 1:6 ){ # - if( i %in% which(plotTF>0) ){ - Cov_cc = VAST:::calc_cov( L_z=ParHat[c('L_omega1_z','L_epsilon1_z','L_beta1_z','L_omega2_z','L_epsilon2_z','L_beta2_z')][[i]], n_f=as.vector(Data[["FieldConfig"]])[i], n_c=Data$n_c ) - plot_cov( Cov=convert(Cov_cc)[category_order,category_order], names=list(category_names[category_order],NA)[[ifelse(i==1|i==3|Dim[2]==1,1,2)]], names2=list(1:nrow(Cov_cc),NA)[[ifelse(i==1|i==2,1,2)]], digits=1, font=2 ) - #if(i==1 | Dim[1]==1) mtext(side=3, text="Spatial", line=1.5, font=2) - #if(i==2 | Dim[1]==1) mtext(side=3, text="Spatio-temporal", line=1.5, font=2) - #if(i==2 | (Dim[2]==1&i==1)) mtext(side=4, text=ifelse(length(Data$ObsModel)==1||Data$ObsModel[2]==0,"Encounter probability","Component #1"), line=0.5, font=2) - #if(i==4 | (Dim[2]==1&i==3)) mtext(side=4, text=ifelse(length(Data$ObsModel)==1||Data$ObsModel[2]==0,"Positive catch rate","Component #2"), line=0.5, font=2) - } - #if( length(Return[[paste0( "Cov_", c("omega1", "epsilon1", "omega2", "epsilon2")[i])]])==0 ){ - # Return[[paste0( "Cov_", c("omega1", "epsilon1", "omega2", "epsilon2")[i])]] = Cov_cc - # if( !is.null(Cov_cc)) Return[[paste0( "Cor_", c("omega1", "epsilon1", "omega2", "epsilon2")[i])]] = cov2cor(Cov_cc) - #} - } - dev.off() - -# # Plot sample -# ThorsonUtilities::save_fig( file=paste0(plotdir,figname,"--Sample.png"), width=Dim[2]*4+1, height=Dim[1]*4, ... ) -# par(mfrow=Dim, mar=c(0,1,1,0), mgp=mgp, tck=tck, oma=oma) -# for(i in which(plotTF>0) ){ -# if(i==1) Cov_cc = cov(Report$Omega1_sc) -# if(i==2) Cov_cc = cov(apply(Report$Epsilon1_sct,MARGIN=2,FUN=as.vector)) -# if(i==3) Cov_cc = -# if(i==4) Cov_cc = cov(Report$Omega2_sc) -# if(i==5) Cov_cc = cov(apply(Report$Epsilon2_sct,MARGIN=2,FUN=as.vector)) -# if(i==6) -# plot_cov( Cov=convert(Cov_cc)[category_order,category_order], names=list(category_names[category_order],NA)[[ifelse(i==1|i==3|Dim[2]==1,1,2)]], names2=list(1:nrow(Cov_cc),NA)[[ifelse(i==1|i==2,1,2)]], digits=1, font=2 ) -# if(i==1 | Dim[1]==1) mtext(side=3, text="Spatial", line=1.5, font=2) -# if(i==2 | Dim[1]==1) mtext(side=3, text="Spatio-temporal", line=1.5, font=2) -# if(i==2 | (Dim[2]==1&i==1)) mtext(side=4, text=ifelse(length(Data$ObsModel)==1||Data$ObsModel[2]==0,"Encounter probability","Component #1"), line=0.5, font=2) -# if(i==4 | (Dim[2]==1&i==3)) mtext(side=4, text=ifelse(length(Data$ObsModel)==1||Data$ObsModel[2]==0,"Positive catch rate","Component #2"), line=0.5, font=2) -# } -# dev.off() - } - - # Return - return( invisible(Return) ) -} - diff --git a/R/calc_coherence.R b/R/calc_coherence.R deleted file mode 100644 index bd4a80c..0000000 --- a/R/calc_coherence.R +++ /dev/null @@ -1,92 +0,0 @@ - -#' Calculate stability metrics -#' -#' \code{Coherence} calculates buffering (`phi`) and coherence (`psi`) -#' -#' @inheritParams Plot_Overdispersion -#' @inheritParams Data_Fn -#' @param covhat estimated covariance used for calculating coherence - -#' @return Tagged list containing measures of synchrony -#' \describe{ -#' \item{phi_xz}{Synchrony index for each site (x) and each period (row of \code{yearbounds_zz})} -#' \item{phi_z}{weighted-average of \code{phi_xz} for each period, weighted by average community-abundance at each site in that period} -#' \item{psi}{Measure of proportion of variance explained by leading eigen-vectors} -#' \item{L_c}{Cholesky decomposition of \code{covhat}} -#' } - -#' @export -calc_coherence = function( Report, Data, covhat=NULL, yearbounds_zz=matrix(c(1,Data$n_t),nrow=1) ){ - - ################## - # Synchrony - ################## - n_z = nrow(yearbounds_zz) - - # Category-specific density ("D"), mean and variance - varD_xcz = array( NA, dim=c(Data$n_x,Data$n_c,n_z) ) - - # Community density ("C") summing across categories - C_xt = array(0, dim=c(Data$n_x,Data$n_t)) - varC_xz = array(NA, dim=c(Data$n_x,n_z)) - - # Area-weighted total biomass ("B") for each category - B_ct = array(0, dim=c(Data$n_c,Data$n_t)) - B_t = rep(0, Data$n_t) - - # Proportion of total biomass ("P") for each station - P_xz = array(NA, dim=c(Data$n_x,n_z)) - - # Synchrony indices - phi_xz = array(NA, dim=c(Data$n_x,n_z)) - phi_z = rep(0, n_z) - - # Temporary variables - temp_xt = array(NA, dim=c(Data$n_x,Data$n_t)) - temp_c = rep(NA, Data$n_c) - - # Derived quantities - for( tI in 1:Data$n_t ){ - for( cI in 1:Data$n_c ){ - for( xI in 1:Data$n_x ){ - C_xt[xI,tI] = C_xt[xI,tI] + Report$D_xct[xI,cI,tI] - B_ct[cI,tI] = B_ct[cI,tI] + Report$D_xct[xI,cI,tI] * Data$a_xl[xI,1] - } - B_t[tI] = B_t[tI] + B_ct[cI,tI] - } - } - - # Loop through periods (only using operations available in TMB, i.e., var, mean, row, col, segment) - for( zI in 1:n_z){ - for( xI in 1:Data$n_x){ - # Variance for each category - for( cI in 1:Data$n_c ){ - for( tI in 1:Data$n_t ) temp_xt[xI,tI] = Report$D_xct[xI,cI,tI] - varD_xcz[xI,cI,zI] = var(temp_xt[xI,][yearbounds_zz[zI,1]:yearbounds_zz[zI,2]]) - } - # Variance for combined across categories - varC_xz[xI,zI] = var(C_xt[xI,][yearbounds_zz[zI,1]:yearbounds_zz[zI,2]]) - # Proportion in each category - P_xz[xI,zI] = Data$a_xl[xI,1] * mean( C_xt[xI,][yearbounds_zz[zI,1]:yearbounds_zz[zI,2]] / B_t[yearbounds_zz[zI,1]:yearbounds_zz[zI,2]] ) - # Synchrony index - for( cI in 1:Data$n_c ) temp_c[cI] = varD_xcz[xI,cI,zI] - phi_xz[xI,zI] = varC_xz[xI,zI] / sum(sqrt(temp_c))^2 - phi_z[zI] = phi_z[zI] + (phi_xz[xI,zI] * P_xz[xI,zI]) - }} - - ################## - # Coherence - ################## - - # Coherence index - if( !is.null(covhat) ){ - L_c = eigen(covhat)$values - psi = 2 * (mean(cumsum(L_c)/sum(L_c))-0.5) - }else{ - L_c = psi = NULL - } - - # Return stuff - Return = list("phi_xz"=phi_xz, "phi_z"=phi_z, "psi"=psi, "P_xz"=P_xz, "L_c"=L_c) - return( Return ) -} diff --git a/R/calc_cov.R b/R/calc_cov.R deleted file mode 100644 index b7f71bf..0000000 --- a/R/calc_cov.R +++ /dev/null @@ -1,45 +0,0 @@ - -#' Calculate covariance -#' -#' \code{calc_cov} calculates loadings matrix and covariance -#' -#' @param L_z vector of loadings parameters -#' @param n_f number of factors (columns of loadings matrix) -#' @param n_c number of variables (rows of loadings matrix) -#' @param returntype character-string matching output - -#' @export -calc_cov = function( L_z, n_f, n_c, returntype="covariance_matrix" ){ - loadings_matrix = function( L_val, n_rows, n_cols ){ - L_rc = matrix(0, nrow=n_rows, ncol=n_cols); - Count = 1; - for( rI in 1:n_rows ){ - for( cI in 1:n_cols ){ - if( rI >= cI ){ - L_rc[rI,cI] = L_val[Count] - Count = Count + 1 - } - }} - return( L_rc) - } - - if( n_f<0 ){ - stop("No covariance when n_f<0, i.e., when a component is turned off") - } - if( n_f==0 ){ - if( length(L_z)!=2 ) stop("Problem with length of L_z given that n_f=0") - Dist_cc = outer( 1:n_c, 1:n_c, FUN=function(a,b){abs(a-b)}) - Cov_cc = L_z[2] ^ Dist_cc - L_cf = NULL - } - if( n_f>0 ){ - if( length(L_z)!=sum(n_c:(n_c-n_f+1)) ) stop("Problem with length of L_z given that n_f>0") - L_cf = loadings_matrix( L_z, n_c, n_f ) - Cov_cc = L_cf %*% t(L_cf) - } - - # Return what is requested - if( returntype=="covariance_matrix" ) Return = Cov_cc - if( returntype=="loadings_matrix" ) Return = L_cf - return( Return ) -} diff --git a/R/calc_synchrony.R b/R/calc_synchrony.R deleted file mode 100644 index c510c1c..0000000 --- a/R/calc_synchrony.R +++ /dev/null @@ -1,140 +0,0 @@ - -#' Calculate synchrony among species or locations -#' -#' \code{calc_synchrony} calculates synchrony -#' -#' @inheritParams Plot_Overdispersion -#' @inheritParams Data_Fn - -#' @return Tagged list containing measures of synchrony -#' \describe{ -#' \item{phi_xz}{Synchrony index for each site (x) and each period (row of \code{yearbounds_zz})} -#' \item{phi_z}{weighted-average of \code{phi_xz} for each period, weighted by average community-abundance at each site in that period} -#' } - -#' @export -calc_synchrony = function( Report, Data, yearbounds_zz=matrix(c(1,Data$n_t),nrow=1) ){ - - # Index lengths - n_z = nrow(yearbounds_zz) - n_t = Data$n_t - n_c = Data$n_c - n_x = Data$n_x - n_y = nrow(Data$t_yz) - - # Extract elements - pow = function(a,b) a^b - D_xcy = Report$D_xcy - a_xl = Data$a_xl - - # Density ("D") or area-expanded total biomass ("B") for each category (use B when summing across sites) - D_xy = matrix( 0, nrow=n_x, ncol=n_y ); - B_cy = matrix( 0, nrow=n_c, ncol=n_y ); - B_y = rep( 0, n_y ); - # Mean - meanD_xcz = array( 0, dim=c(n_x,n_c,n_z) ); - meanD_xz = matrix( 0, nrow=n_x, ncol=n_z ); - meanB_cz = matrix( 0, nrow=n_c, ncol=n_z ); - meanB_z = rep( 0, n_z ); - # Sample variance in category-specific density ("D") and biomass ("B") - varD_xcz = array( 0, dim=c(n_x,n_c,n_z) ); - varD_xz = matrix( 0, nrow=n_x, ncol=n_z ); - varB_cz = matrix( 0, nrow=n_c, ncol=n_z ); - varB_z = rep( 0, n_z ); - varB_xbar_z = rep( 0, n_z ); - varB_cbar_z = rep( 0, n_z ); - maxsdD_xz = matrix( 0, nrow=n_x, ncol=n_z ); - maxsdB_cz = matrix( 0, nrow=n_c, ncol=n_z ); - maxsdB_z = rep( 0, n_z ); - # Proportion of total biomass ("P") for each location or each category - propB_xz = matrix( 0, nrow=n_x, ncol=n_z ); - propB_cz = matrix( 0, nrow=n_c, ncol=n_z ); - # Synchrony indices - phi_xz = matrix( 0, nrow=n_x, ncol=n_z ); - phi_cz = matrix( 0, nrow=n_c, ncol=n_z ); - phi_xbar_z = rep( 0, n_z ); - phi_cbar_z = rep( 0, n_z ); - phi_z = rep( 0, n_z ); - - # Calculate total biomass for different categories - for( yI in 1:n_y ){ - for( cI in 1:n_c ){ - for( xI in 1:n_x ){ - D_xy[xI,yI] = D_xy[xI,yI] + D_xcy[xI,cI,yI]; - B_cy[cI,yI] = B_cy[cI,yI] + D_xcy[xI,cI,yI] * a_xl[xI,1]; - B_y[yI] = B_y[yI] + D_xcy[xI,cI,yI] * a_xl[xI,1]; - } - } - } - # Loop through periods (only using operations available in TMB, i.e., var, mean, row, col, segment) - for( zI in 1:n_z ){ - for( xI in 1:n_x ){ - # Variance for biomass in each category, use sum(diff^2)/(length(diff)-1) where -1 in denominator is the sample-variance Bessel correction - for( cI in 1:n_c ){ - temp_mean = 0; - for( yI in yearbounds_zz[zI,1]:yearbounds_zz[zI,2] ) meanD_xcz[xI,cI,zI] = meanD_xcz[xI,cI,zI] + D_xcy[xI,cI,yI] / (yearbounds_zz[zI,2]-yearbounds_zz[zI,1]+1); - for( yI in yearbounds_zz[zI,1]:yearbounds_zz[zI,2] ){ - varD_xcz[xI,cI,zI] = varD_xcz[xI,cI,zI] + pow(D_xcy[xI,cI,yI]-meanD_xcz[xI,cI,zI],2) / (yearbounds_zz[zI,2]-yearbounds_zz[zI,1]); - } - } - # Variance for combined biomass across categories, use sum(diff^2)/(length(diff)-1) where -1 in denominator is the sample-variance Bessel correction - for( yI in yearbounds_zz[zI,1]:yearbounds_zz[zI,2] ) meanD_xz[xI,zI] = meanD_xz[xI,zI] + D_xy[xI,yI] / (yearbounds_zz[zI,2]-yearbounds_zz[zI,1]+1); - for( yI in yearbounds_zz[zI,1]:yearbounds_zz[zI,2] ) { - varD_xz[xI,zI] = varD_xz[xI,zI] + pow(D_xy[xI,yI]-meanD_xz[xI,zI],2) / (yearbounds_zz[zI,2]-yearbounds_zz[zI,1]); - } - } - for( cI in 1:n_c ){ - # Variance for combined biomass across sites, use sum(diff^2)/(length(diff)-1) where -1 in denominator is the sample-variance Bessel correction - for( yI in yearbounds_zz[zI,1]:yearbounds_zz[zI,2] ) meanB_cz[cI,zI] = meanB_cz[cI,zI] + B_cy[cI,yI] / (yearbounds_zz[zI,2]-yearbounds_zz[zI,1]+1); - for( yI in yearbounds_zz[zI,1]:yearbounds_zz[zI,2] ) { - varB_cz[cI,zI] = varB_cz[cI,zI] + pow(B_cy[cI,yI]-meanB_cz[cI,zI],2) / (yearbounds_zz[zI,2]-yearbounds_zz[zI,1]); - } - } - # Variance for combined biomass across sites and categories, use sum(diff^2)/(length(diff)-1) where -1 in denominator is the sample-variance Bessel correction - for( yI in yearbounds_zz[zI,1]:yearbounds_zz[zI,2] ) meanB_z[zI] = meanB_z[zI] + B_y[yI] / (yearbounds_zz[zI,2]-yearbounds_zz[zI,1]+1); - for( yI in yearbounds_zz[zI,1]:yearbounds_zz[zI,2] ) { - varB_z[zI] = varB_z[zI] + pow(B_y[yI]-meanB_z[zI],2) / (yearbounds_zz[zI,2]-yearbounds_zz[zI,1]); - } - # Proportion in each site - for( xI in 1:n_x ){ - for( yI in yearbounds_zz[zI,1]:yearbounds_zz[zI,2] ) { - propB_xz[xI,zI] = propB_xz[xI,zI] + a_xl[xI,1] * D_xy[xI,yI] / B_y[yI] / (yearbounds_zz[zI,2]-yearbounds_zz[zI,1]+1); - } - } - # Proportion in each category - for( cI in 1:n_c ){ - for( yI in yearbounds_zz[zI,1]:yearbounds_zz[zI,2] ) { - propB_cz[cI,zI] = propB_cz[cI,zI] + B_cy[cI,yI] / B_y[yI] / (yearbounds_zz[zI,2]-yearbounds_zz[zI,1]+1); - } - } - # Species-buffering index (calculate in Density so that areas with zero area are OK) - for( xI in 1:n_x ){ - for( cI in 1:n_c ){ - maxsdD_xz[xI,zI] = maxsdD_xz[xI,zI] + pow(varD_xcz[xI,cI,zI], 0.5); - } - phi_xz[xI,zI] = varD_xz[xI,zI] / pow( maxsdD_xz[xI,zI], 2); - varB_xbar_z[zI] = varB_xbar_z[zI] + pow(a_xl[xI,1],2) * varD_xz[xI,zI] * propB_xz[xI,zI]; - phi_xbar_z[zI] = phi_xbar_z[zI] + phi_xz[xI,zI] * propB_xz[xI,zI]; - } - # Spatial-buffering index - for( cI in 1:n_c ){ - for( xI in 1:n_x ){ - maxsdB_cz[cI,zI] = maxsdB_cz[cI,zI] + a_xl[xI,1] * pow(varD_xcz[xI,cI,zI], 0.5); - } - phi_cz[cI,zI] = varB_cz[cI,zI] / pow( maxsdB_cz[cI,zI], 2); - varB_cbar_z[zI] = varB_cbar_z[zI] + varB_cz[cI,zI] * propB_cz[cI,zI]; - phi_cbar_z[zI] = phi_cbar_z[zI] + phi_cz[cI,zI] * propB_cz[cI,zI]; - } - # Spatial and species-buffering index - for( cI in 1:n_c ){ - for( xI in 1:n_x ){ - maxsdB_z[zI] = maxsdB_z[zI] + a_xl[xI,1] * pow(varD_xcz[xI,cI,zI], 0.5); - } - } - phi_z[zI] = varB_z[zI] / pow( maxsdB_z[zI], 2); - } - - # Return stuff - Return = list( "phi_z"=phi_z, "phi_xz"=phi_xz, "phi_cz"=phi_cz, "phi_xbar_z"=phi_xz, "phi_cbar_z"=phi_cz, "B_y"=B_y, "B_cy"=B_cy, "D_xy"=D_xy, "D_xcy"=D_xcy) - return( Return ) -} diff --git a/R/calculate_proportion.R b/R/calculate_proportion.R deleted file mode 100644 index a417ba4..0000000 --- a/R/calculate_proportion.R +++ /dev/null @@ -1,75 +0,0 @@ - - -#' Calculate the compositional-expansion -#' -#' \code{calculate_proportion} takes output from a VAST run and calculates the proportion of biomass in different categories -#' -#' @param Index output from \code{FishStatsUtils::plot_biomass_index} -#' @inheritParams FishStatsUtils::plot_biomass_index -#' @param ... list of settings to pass to \code{sdreport} -#' -#' @return Tagged list of output -#' \describe{ -#' \item{Prop_ctl}{Proportion of biomass for each category c, time t, and stratum l} -#' \item{Neff_tl}{Effective sample size (median across categories) for each time t and stratum l} -#' } -#' -#' @export -calculate_proportion = function( TmbData, Index, Year_Set=NULL, Years2Include=NULL, strata_names=NULL, category_names=NULL, plot_legend=TRUE, - DirName=paste0(getwd(),"/"), PlotName="Proportion.png", interval_width=1, width=6, height=6, ... ){ - - # Warnings and errors - if( !all(TmbData[['FieldConfig']] %in% c(-2,-1)) ){ - stop("Derivation only included for independent categories") - } - Index_ctl = array(Index$Index_ctl[,,,'Estimate'],dim=dim(Index$Index_ctl)[1:3]) - SE_Index_ctl = array(Index$Index_ctl[,,,'Std. Error'],dim=dim(Index$Index_ctl)[1:3]) - - # Calculate proportions, and total biomass - Prop_ctl = Index_ctl / outer(rep(1,TmbData$n_c),apply(Index_ctl,MARGIN=2:3,FUN=sum)) - Index_tl = apply(Index_ctl,MARGIN=2:3,FUN=sum) - SE_Index_tl = sqrt(apply(SE_Index_ctl^2,MARGIN=2:3,FUN=sum,na.rm=TRUE)) - - # Approximate variance for proportions, and effective sample size - Neff_ctl = var_Prop_ctl = array(NA,dim=dim(Prop_ctl)) - for( cI in 1:dim(var_Prop_ctl)[1]){ - for( tI in 1:dim(var_Prop_ctl)[2]){ - for( lI in 1:dim(var_Prop_ctl)[3]){ - # Original version - #var_Prop_ctl[cI,tI,lI] = Index_ctl[cI,tI,lI]^2/Index_tl[tI,lI]^2 * (SE_Index_ctl[cI,tI,lI]^2/Index_ctl[cI,tI,lI]^2 + SE_Index_tl[tI,lI]^2/Index_tl[tI,lI]^2 ) - # Slightly extended version - var_Prop_ctl[cI,tI,lI] = Index_ctl[cI,tI,lI]^2/Index_tl[tI,lI]^2 * (SE_Index_ctl[cI,tI,lI]^2/Index_ctl[cI,tI,lI]^2 - 2*SE_Index_ctl[cI,tI,lI]^2/(Index_ctl[cI,tI,lI]*Index_tl[tI,lI]) + SE_Index_tl[tI,lI]^2/Index_tl[tI,lI]^2 ) - # Covert to effective sample size - Neff_ctl[cI,tI,lI] = Prop_ctl[cI,tI,lI] * (1-Prop_ctl[cI,tI,lI]) / var_Prop_ctl[cI,tI,lI] - }}} - - # Median effective sample size across categories - Neff_tl = apply(Neff_ctl, MARGIN=2:3, FUN=median, na.rm=TRUE) - - # Fill in missing - if( is.null(Year_Set) ) Year_Set = 1:TmbData$n_t - if( is.null(Years2Include) ) Years2Include = 1:TmbData$n_t - if( is.null(strata_names) ) strata_names = 1:TmbData$n_l - if( is.null(category_names) ) category_names = 1:TmbData$n_c - - # Plot - Par = list( mar=c(2,2,1,0), mgp=c(2,0.5,0), tck=-0.02, yaxs="i", oma=c(2,2,0,0), mfrow=c(ceiling(sqrt(TmbData$n_t)),ceiling(TmbData$n_t/ceiling(sqrt(TmbData$n_t)))), ... ) - png( file=paste0(DirName,"/",PlotName), width=width, height=height, res=200, units="in") - par( Par ) - for( tI in 1:TmbData$n_t ){ - # Calculate y-axis limits - Ylim = c(0, max(Prop_ctl[,tI,]%o%c(1,1) + sqrt(var_Prop_ctl[,tI,])%o%c(-interval_width,interval_width),na.rm=TRUE)) - # Plot stuff - plot(1, type="n", xlim=range(category_names), ylim=1.05*Ylim, xlab="", ylab="", main=ifelse(TmbData$n_t>1,paste0("Year ",Year_Set[tI]),"") ) - for(l in 1:TmbData$n_l){ - FishStatsUtils::plot_lines( y=Prop_ctl[,tI,l], x=1:TmbData$n_c+seq(-0.1,0.1,length=TmbData$n_l)[l], ybounds=Prop_ctl[,tI,]%o%c(1,1) + sqrt(var_Prop_ctl[,tI,])%o%c(-interval_width,interval_width), type="b", col=rainbow(TmbData[['n_l']])[l], col_bounds=rainbow(TmbData[['n_l']])[l], ylim=Ylim) - } - if(plot_legend==TRUE) legend( "top", bty="n", fill=rainbow(TmbData[['n_l']]), legend=as.character(strata_names), ncol=2 ) - } - mtext( side=1:2, text=c("Age","Proportion of biomass"), outer=TRUE, line=c(0,0) ) - dev.off() - - # Return stuff - Return = list("Prop_ctl"=Prop_ctl, "Neff_tl"=Neff_tl, "var_Prop_ctl"=var_Prop_ctl, "Index_tl"=Index_tl, "Neff_ctl"=Neff_ctl) - return( invisible(Return) ) -} diff --git a/R/plot_eigen.R b/R/plot_eigen.R deleted file mode 100644 index 7f6b9c3..0000000 --- a/R/plot_eigen.R +++ /dev/null @@ -1,17 +0,0 @@ - -#' @export -plot_eigen = function( Cov, which2plot=1:min(3,ncol(Cov)), names=1:nrow(Cov), digits=2, las=2, add=FALSE, ... ){ - Eigen = eigen(Cov) - rownames( Eigen$vectors ) = names - if(add==FALSE) par( mfrow=par()$mfrow, oma=par()$oma, mar=par()$mar, tck=par()$tck ) - for( cI in which2plot ){ - plot( 1, type="n", xlim=c(0.5,ncol(Cov)+0.5), ylim=c(-1,1.2), xlab="", ylab="", xaxt="n", ... ) - for( rI in 1:nrow(Cov)){ - graphics::lines(y=c(0,Eigen$vectors[rI,cI])*sign(Eigen$vectors[which.max(abs(Eigen$vectors[,cI])),cI]), x=rep(rI,2), lwd=5) - } - graphics::abline( h=0, lty="dotted" ) - graphics::legend( "top", bty="n", legend=paste0("Eigenvalue = ",formatC(Eigen$values[cI],format="f",digits=2)) ) - axis(1, at=1:ncol(Cov), labels=names, las=las ) - } - return( invisible(Eigen) ) -} diff --git a/R/Summarize.R b/R/summarize_results.R similarity index 100% rename from R/Summarize.R rename to R/summarize_results.R diff --git a/R/zzz.R b/R/zzz.R index 3c9ffc8..12571e8 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -48,3 +48,47 @@ Data_Fn = function( ... ){ VAST::make_data( ... ) } +#' Copy of FishStatsUtils::plot_factors +#' +#' Included for continuity when using old scripts +#' +#' Please use \code{?FishStatsUtils::plot_factors} to see list of arguments and usage +#' @export +Plot_factors = function( ... ){ + .Deprecated( new="FishStatsUtils::plot_factors" ) + FishStatsUtils::plot_factors( ... ) +} + +#' Copy of FishStatsUtils::plot_overdispersion +#' +#' Included for continuity when using old scripts +#' +#' Please use \code{?FishStatsUtils::plot_overdispersion} to see list of arguments and usage +#' @export +Plot_Overdispersion = function( ... ){ + .Deprecated( new="FishStatsUtils::plot_overdispersion" ) + FishStatsUtils::plot_overdispersion( ... ) +} + +#' Copy of FishStatsUtils::summarize_covariace +#' +#' Included for continuity when using old scripts +#' +#' Please use \code{?FishStatsUtils::summarize_covariace} to see list of arguments and usage +#' @export +Summarize_Covariance = function( ... ){ + .Deprecated( new="FishStatsUtils::summarize_covariace" ) + FishStatsUtils::summarize_covariace( ... ) +} + +#' Copy of VAST::summarize_results +#' +#' Included for continuity when using old scripts +#' +#' Please use \code{?VAST::summarize_results} to see list of arguments and usage +#' @export +Summarize = function( ... ){ + .Deprecated( new="VAST::summarize_results" ) + VAST::summarize_results( ... ) +} + diff --git a/man/Coherence.Rd b/man/Coherence.Rd deleted file mode 100644 index 44d4a68..0000000 --- a/man/Coherence.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Coherence.R -\name{Coherence} -\alias{Coherence} -\title{Calculate stability metrics} -\usage{ -Coherence(Report, Data, covhat = NULL, yearbounds_zz = matrix(c(1, - Data$n_t), nrow = 1)) -} -\arguments{ -\item{Report}{output report, e.g., from \code{Report <- obj$report()}} - -\item{Data}{tagged list of input data} - -\item{covhat}{estimated covariance used for calculating coherence} -} -\value{ -Tagged list containing measures of synchrony -\describe{ - \item{phi_xz}{Synchrony index for each site (x) and each period (row of \code{yearbounds_zz})} - \item{phi_z}{weighted-average of \code{phi_xz} for each period, weighted by average community-abundance at each site in that period} - \item{psi}{Measure of proportion of variance explained by leading eigen-vectors} - \item{L_c}{Cholesky decomposition of \code{covhat}} -} -} -\description{ -\code{Coherence} calculates buffering (`phi`) and coherence (`psi`) -} diff --git a/man/Crossvalidate_Fn.Rd b/man/Crossvalidate_Fn.Rd deleted file mode 100644 index c00d486..0000000 --- a/man/Crossvalidate_Fn.Rd +++ /dev/null @@ -1,34 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Crossvalidate_Fn.R -\name{Crossvalidate_Fn} -\alias{Crossvalidate_Fn} -\title{K-fold crossvalidation} -\usage{ -Crossvalidate_Fn(record_dir, parhat, original_data, group_i = NULL, - kfold = 10, newtonsteps = 1, skip_finished = FALSE, ...) -} -\arguments{ -\item{record_dir, }{a directory with writing privilege where the runs are stored} - -\item{parhat, }{a tagged list of parameters from the fitted model, e.g., as generated by \code{parhat <- obj$env$parList()} after covergence} - -\item{original_data, }{a tagged list of data for the VAST model} - -\item{group_i, }{a vector of positive integers, indicating the k-fold group for each observation (default=NULL, which generates a new group_i with even probability)} - -\item{kfold, }{the number of crossvalidation batches used (default=10)} - -\item{newtonsteps}{number of extra newton steps to take after optimization (alternative to \code{loopnum})} - -\item{skip_finished}{boolean specifying whether to rerun (skip_finished==FALSE) or skip (skip_finished==TRUE) previously completed runs (Default=FALSE)} - -\item{...}{Additional arguments to pass to \code{VAST::Build_TMB_Fn}} - -\item{skip_finished}{boolean specifying whether to rerun (skip_finished==FALSE) or skip (skip_finished==TRUE) previously completed runs (Default=FALSE)} -} -\value{ -Results a matrix with total predictive negative log-likelihood for each crossvalidation partition, and number of crossvalidation samples for that partition -} -\description{ -\code{Crossvalidate_Fn} runs a k-fold crossvalidation analysis on a fitted model object -} diff --git a/man/Plot_Overdispersion.Rd b/man/Plot_Overdispersion.Rd index c6ecd5a..d0f2c78 100644 --- a/man/Plot_Overdispersion.Rd +++ b/man/Plot_Overdispersion.Rd @@ -1,39 +1,14 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Plot_Overdispersion.R +% Please edit documentation in R/zzz.R \name{Plot_Overdispersion} \alias{Plot_Overdispersion} -\title{Plot estimated overdispersion} +\title{Copy of FishStatsUtils::plot_overdispersion} \usage{ -Plot_Overdispersion(filename1, filename2, Data, ParHat, Report, - SD = NULL, Map = NULL, ControlList1 = list(Width = 8, Height = 4, - Res = 200, Units = "in"), ControlList2 = ControlList1) -} -\arguments{ -\item{filename1}{filename (including absolute path) for overdispersion for component #1} - -\item{filename2}{filename (including absolute path) for overdispersion for component #2} - -\item{Data}{tagged list of input data} - -\item{ParHat}{Tagged list of fitted data, e.g., from \code{ParHat <- obj$env$parList()}} - -\item{Report}{output report, e.g., from \code{Report <- obj$report()}} - -\item{SD}{standard deviation report, e.g., from \code{SD <- sdreport( obj )}} - -\item{Map}{tagged list of map inputs} - -\item{ControlList1}{plotting size for component #1} - -\item{ControlIist2}{plotting size for component #2 (default: ControlList2 <- ControlList1)} -} -\value{ -Tagged list containing inputs to function Build_TMB_Fn() -\describe{ - \item{Cov1_cc}{Covariance for component #1 (typically probability of occurence)} - \item{Cov2_cc}{Covariance for component #2 (typically positive catch rates)} -} +Plot_Overdispersion(...) } \description{ -\code{Plot_Overdispersion} plots sample and analytic estimates of overdispersion +Included for continuity when using old scripts +} +\details{ +Please use \code{?FishStatsUtils::plot_overdispersion} to see list of arguments and usage } diff --git a/man/Plot_factors.Rd b/man/Plot_factors.Rd index 42b00da..86beacd 100644 --- a/man/Plot_factors.Rd +++ b/man/Plot_factors.Rd @@ -1,37 +1,14 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Plot_factors.R +% Please edit documentation in R/zzz.R \name{Plot_factors} \alias{Plot_factors} -\title{Plot factor-decomposition of covariance} +\title{Copy of FishStatsUtils::plot_factors} \usage{ -Plot_factors(Report, ParHat, Data, SD, Year_Set = NULL, - category_names = NULL, RotationMethod = "PCA", - mapdetails_list = NULL, Dim_year = NULL, Dim_species = NULL, - plotdir = paste0(getwd(), "/"), land_color = "grey") -} -\arguments{ -\item{Report}{output report, e.g., from \code{Report <- obj$report()}} - -\item{ParHat}{Tagged list of fitted data, e.g., from \code{ParHat <- obj$env$parList()}} - -\item{Data}{tagged list of input data} - -\item{SD}{standard deviation report, e.g., from \code{SD <- sdreport( obj )}} - -\item{Year_Set}{plotting-names for time dimension} - -\item{category_names}{character-vector listing name for each category} - -\item{mapdetails_list}{output from \code{FishStatsUtils::MapDetails_Fn}} - -\item{Dim_year}{Plotting dimension (row,column) for plot of years (default: square with sufficient size for number of years)} - -\item{Dim_species}{Plotting dimension (row,column) for plot of categories (default: square with sufficient size for number of categories)} - -\item{plotdir}{directory for saving plots} - -\item{land_color}{color for filling in land (use \code{land_color=rgb(0,0,0,alpha=0)} for transparent land)} +Plot_factors(...) } \description{ -\code{Plot_factors} plots factor loadings, average spatial factors, and spatio-temporal factors +Included for continuity when using old scripts +} +\details{ +Please use \code{?FishStatsUtils::plot_factors} to see list of arguments and usage } diff --git a/man/Rerun_Fn.Rd b/man/Rerun_Fn.Rd deleted file mode 100644 index 4dcb4d3..0000000 --- a/man/Rerun_Fn.Rd +++ /dev/null @@ -1,53 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Rerun_Fn.R -\name{Rerun_Fn} -\alias{Rerun_Fn} -\title{Explore counter-factual scenario} -\usage{ -Rerun_Fn(parhat0, turnoff_pars, loc_x, - cov_to_turnoff = 1:dim(parhat0[["gamma2_ctp"]])[3], - calculate_COG = TRUE, figname = NULL, Map = "generate", - MapDetails_List = NULL, year_set = 1:ncol(parhat0[["beta1_ct"]]), - c_set = 1:nrow(parhat0[["beta1_ct"]]), ...) -} -\arguments{ -\item{parhat0}{parameter values to use by default (presumably maximum-likelihood estimates)} - -\item{turnoff_pars}{character-vector of parameters to turn off} - -\item{loc_x}{location for each knot used to calculate center-of-gravity for counter-factual runs with colnames c('E_km','N_km','Lon','Lat')} - -\item{cov_to_turnoff}{which covariates to turn off, as indicated by their order in \code{TmbData$X_xtp} (which only matters if "gamma1_ctp" or "gamma2_ctp" is in \code{turnoff_pars})} - -\item{calculate_COG}{Boolean whether to calculate COG for each run} - -\item{figname}{name for figure to plot density in counter-factual scenario} - -\item{MapDetails_List}{output from \code{FishStatsUtils::MapDetails_Fn}} - -\item{year_set}{set of parameters to include} - -\item{c_set}{set of categories to include} - -\item{...}{additional arguments passed to \code{VAST::Build_TMB_Fn}} -} -\value{ -Tagged list -\describe{ - \item{Report}{Report output for counter-factual run} - \item{NewBuild_List}{Output from \code{VAST::Build_TMB_Fn} using counter-factual parameters} -} -} -\description{ -\code{Rerun_Fn} re-builds a model while fixing a subset of parameters at zero -} -\examples{ -\dontrun{ -# Run without GMRF - -Rerun_Fn(parhat0 = Obj$env$parList(), - turnoff_pars = c("Epsiloninput1_sft", "Epsiloninput2_sft"), - loc_x = Spatial_List$loc_x, - TmbData = TmbData, Version = "VAST_v4_0_0") -} -} diff --git a/man/Summarize.Rd b/man/Summarize.Rd index a62c34d..1f7c261 100644 --- a/man/Summarize.Rd +++ b/man/Summarize.Rd @@ -1,10 +1,12 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Summarize.R +% Please edit documentation in R/summarize_results.R, R/zzz.R \name{Summarize} \alias{Summarize} \title{Summarize and explain results} \usage{ -Summarize(obj) +Summarize(...) + +Summarize(...) } \arguments{ \item{obj}{the TMB object after it has been previously optimized} @@ -18,7 +20,11 @@ Tagged list of useful output } \description{ \code{Summarize} is intended to help document results, by providing explanation for outputs from \code{obj$report()} + +Included for continuity when using old scripts } \details{ Please post on the GitHub issue tracker if you'd like for another specific output to be added and documented here + +Please use \code{?VAST::summarize_results} to see list of arguments and usage } diff --git a/man/Summarize_Covariance.Rd b/man/Summarize_Covariance.Rd index 25bc69d..70bd6b8 100644 --- a/man/Summarize_Covariance.Rd +++ b/man/Summarize_Covariance.Rd @@ -1,44 +1,14 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Summarize_Covariance.R +% Please edit documentation in R/zzz.R \name{Summarize_Covariance} \alias{Summarize_Covariance} -\title{Explore spatio-temporal covariance} +\title{Copy of FishStatsUtils::summarize_covariace} \usage{ -Summarize_Covariance(Report, Data, ParHat, SD = NULL, - category_order = 1:Data$n_c, category_names = 1:Data$n_c, - plotdir = paste0(getwd(), "/"), figname = "Cov", plotTF = NULL, - plot_cor = TRUE, mgp = c(2, 0.5, 0), tck = -0.02, oma = c(0, 5, - 2, 0), ...) -} -\arguments{ -\item{Report}{output report, e.g., from \code{Report <- obj$report()}} - -\item{Data}{tagged list of input data} - -\item{ParHat}{Tagged list of fitted data, e.g., from \code{ParHat <- obj$env$parList()}} - -\item{SD}{standard deviation report, e.g., from \code{SD <- sdreport( obj )}} - -\item{category_order}{integer-vector to re-order categories while plotting} - -\item{category_names}{character-vector listing name for each category} - -\item{plotdir}{directory (absolute path) for plots} - -\item{figname}{character for first-part of figure names} - -\item{plotTF}{integer-vector (length 4) specifying which covariances to plot (recommended: use \code{plotTF=FieldConfig})} - -\item{plot_cor}{Boolean, whether to plot correlation or covariance} - -\item{mgp}{passed to \code{par}} - -\item{tck}{passed to \code{par}} - -\item{oma}{passed to \code{par}} - -\item{...}{passed to \code{ThorsonUtilities::save_fig}} +Summarize_Covariance(...) } \description{ -\code{Summarize_Covariance} plots and returns spatio-temporal covariance among categories +Included for continuity when using old scripts +} +\details{ +Please use \code{?FishStatsUtils::summarize_covariace} to see list of arguments and usage } diff --git a/man/calc_coherence.Rd b/man/calc_coherence.Rd deleted file mode 100644 index 30477cd..0000000 --- a/man/calc_coherence.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calc_coherence.R -\name{calc_coherence} -\alias{calc_coherence} -\title{Calculate stability metrics} -\usage{ -calc_coherence(Report, Data, covhat = NULL, yearbounds_zz = matrix(c(1, - Data$n_t), nrow = 1)) -} -\arguments{ -\item{Report}{output report, e.g., from \code{Report <- obj$report()}} - -\item{Data}{tagged list of input data} - -\item{covhat}{estimated covariance used for calculating coherence} -} -\value{ -Tagged list containing measures of synchrony -\describe{ - \item{phi_xz}{Synchrony index for each site (x) and each period (row of \code{yearbounds_zz})} - \item{phi_z}{weighted-average of \code{phi_xz} for each period, weighted by average community-abundance at each site in that period} - \item{psi}{Measure of proportion of variance explained by leading eigen-vectors} - \item{L_c}{Cholesky decomposition of \code{covhat}} -} -} -\description{ -\code{Coherence} calculates buffering (`phi`) and coherence (`psi`) -} diff --git a/man/calc_cov.Rd b/man/calc_cov.Rd deleted file mode 100644 index c582612..0000000 --- a/man/calc_cov.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calc_cov.R -\name{calc_cov} -\alias{calc_cov} -\title{Calculate covariance} -\usage{ -calc_cov(L_z, n_f, n_c, returntype = "covariance_matrix") -} -\arguments{ -\item{L_z}{vector of loadings parameters} - -\item{n_f}{number of factors (columns of loadings matrix)} - -\item{n_c}{number of variables (rows of loadings matrix)} - -\item{returntype}{character-string matching output} -} -\description{ -\code{calc_cov} calculates loadings matrix and covariance -} diff --git a/man/calc_synchrony.Rd b/man/calc_synchrony.Rd deleted file mode 100644 index 7e6854d..0000000 --- a/man/calc_synchrony.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calc_synchrony.R -\name{calc_synchrony} -\alias{calc_synchrony} -\title{Calculate synchrony among species or locations} -\usage{ -calc_synchrony(Report, Data, yearbounds_zz = matrix(c(1, Data$n_t), nrow - = 1)) -} -\arguments{ -\item{Report}{output report, e.g., from \code{Report <- obj$report()}} - -\item{Data}{tagged list of input data} -} -\value{ -Tagged list containing measures of synchrony -\describe{ - \item{phi_xz}{Synchrony index for each site (x) and each period (row of \code{yearbounds_zz})} - \item{phi_z}{weighted-average of \code{phi_xz} for each period, weighted by average community-abundance at each site in that period} -} -} -\description{ -\code{calc_synchrony} calculates synchrony -} diff --git a/man/calculate_proportion.Rd b/man/calculate_proportion.Rd deleted file mode 100644 index f480f4d..0000000 --- a/man/calculate_proportion.Rd +++ /dev/null @@ -1,49 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calculate_proportion.R -\name{calculate_proportion} -\alias{calculate_proportion} -\title{Calculate the compositional-expansion} -\usage{ -calculate_proportion(TmbData, Index, Year_Set = NULL, - Years2Include = NULL, strata_names = NULL, category_names = NULL, - plot_legend = TRUE, DirName = paste0(getwd(), "/"), - PlotName = "Proportion.png", interval_width = 1, width = 6, - height = 6, ...) -} -\arguments{ -\item{TmbData}{Formatted data inputs, from `VAST::Data_Fn(...)`} - -\item{Index}{output from \code{FishStatsUtils::plot_biomass_index}} - -\item{Year_Set}{Year names for labeling panels} - -\item{Years2Include}{integer vector, specifying positions of \code{Year_Set} for plotting (used to avoid plotting years with no data, etc.)} - -\item{strata_names}{names for spatial strata} - -\item{category_names}{names for categories (if using package `VAST`)} - -\item{plot_legend}{Add legend for labelling colors} - -\item{DirName}{Directory for saving plot and table} - -\item{PlotName}{Name for plot} - -\item{interval_width}{width for confidence intervals} - -\item{width}{plot width in inches} - -\item{height}{plot height in inches} - -\item{...}{list of settings to pass to \code{sdreport}} -} -\value{ -Tagged list of output -\describe{ - \item{Prop_ctl}{Proportion of biomass for each category c, time t, and stratum l} - \item{Neff_tl}{Effective sample size (median across categories) for each time t and stratum l} -} -} -\description{ -\code{calculate_proportion} takes output from a VAST run and calculates the proportion of biomass in different categories -} diff --git a/tests/testthat.R b/tests/testthat.R index 7065dd3..3096075 100644 --- a/tests/testthat.R +++ b/tests/testthat.R @@ -1,4 +1,5 @@ +# devtools::install_github("james-thorson/FishStatsUtils", ref="development") # devtools::install_github("james-thorson/VAST", ref="development") library(testthat) diff --git a/tests/testthat/test-comp-expansion.R b/tests/testthat/test-comp-expansion.R index afc9262..3ea62af 100644 --- a/tests/testthat/test-comp-expansion.R +++ b/tests/testthat/test-comp-expansion.R @@ -25,6 +25,7 @@ test_that("Male lingcod compositional expansion is working ", { expect_equal( as.vector(Par1), as.vector(Par2), tolerance=1e-3 ) expect_equal( opt$objective, Opt$objective, tolerance=1e-3 ) # Get expanded composition estimates and input sample sizes + #Opt = TMBhelper::Optimize( obj=TmbList[["Obj"]], startpar=Opt$par, getsd=TRUE, lower=TmbList[["Lower"]], upper=TmbList[["Upper"]], newtonsteps=0 ) #Index = plot_biomass_index( TmbData=TmbData, Sdreport=Opt$SD, DirName=test_path ) #calculate_proportion( TmbData=TmbData, Index=Index, DirName=test_path, Year_Set=unique(Data_Geostat$Year) ) }) From 33f77c019a5b9527913155a5cdba6fb4ce9f2755 Mon Sep 17 00:00:00 2001 From: "James.Thorson" <1454196437@mil> Date: Mon, 22 Apr 2019 09:20:59 -0700 Subject: [PATCH 24/31] Completing switch to Ropensci... ... naming conventions, using verb_noun with all lower-case --- NAMESPACE | 2 ++ R/Make_Map.R | 2 +- R/make_model.R | 4 +-- R/{Param_Fn.R => make_parameters.R} | 8 ++--- R/zzz.R | 23 ++++++++++++ man/Make_Map.Rd | 14 ++++++++ man/Param_Fn.Rd | 52 ++++----------------------- man/make_parameters.Rd | 56 +++++++++++++++++++++++++++++ 8 files changed, 109 insertions(+), 52 deletions(-) rename R/{Param_Fn.R => make_parameters.R} (99%) create mode 100644 man/Make_Map.Rd create mode 100644 man/make_parameters.Rd diff --git a/NAMESPACE b/NAMESPACE index 5c730f6..fc77a43 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -9,7 +9,9 @@ export(Plot_factors) export(Summarize) export(Summarize_Covariance) export(make_data) +export(make_map) export(make_model) +export(make_parameters) importFrom(grDevices,colorRampPalette) importFrom(grDevices,dev.off) importFrom(grDevices,png) diff --git a/R/Make_Map.R b/R/Make_Map.R index 56ab09b..c2ff437 100644 --- a/R/Make_Map.R +++ b/R/Make_Map.R @@ -1,5 +1,5 @@ #' @export -Make_Map <- +make_map <- function( DataList, TmbParams, RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0), Npool=0 ){ # Local functions diff --git a/R/make_model.R b/R/make_model.R index 3def380..2372538 100644 --- a/R/make_model.R +++ b/R/make_model.R @@ -75,10 +75,10 @@ function( TmbData, Version, RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilo # Parameters # TmbData=TmbData - if( length(Parameters)==1 && Parameters=="generate" ) Parameters = Param_Fn( Version=Version, DataList=TmbData, RhoConfig=RhoConfig ) + if( length(Parameters)==1 && Parameters=="generate" ) Parameters = make_parameters( Version=Version, DataList=TmbData, RhoConfig=RhoConfig ) # Which parameters are turned off - if( length(Map)==1 && Map=="generate" ) Map = Make_Map( DataList=TmbData, TmbParams=Parameters, RhoConfig=RhoConfig, Npool=Npool ) + if( length(Map)==1 && Map=="generate" ) Map = make_map( DataList=TmbData, TmbParams=Parameters, RhoConfig=RhoConfig, Npool=Npool ) # Which are random if( length(Random)==1 && Random=="generate" ){ diff --git a/R/Param_Fn.R b/R/make_parameters.R similarity index 99% rename from R/Param_Fn.R rename to R/make_parameters.R index 86585f9..cdeb55e 100644 --- a/R/Param_Fn.R +++ b/R/make_parameters.R @@ -1,9 +1,9 @@ #' Calculate parameter inputs for TMB #' -#' \code{Param_Fn} generates the \code{parameters} input for \code{TMB::MakeADFun} +#' \code{make_parameters} generates the \code{parameters} input for \code{TMB::MakeADFun} #' -#' @param DataList list outputted from \code{VAST::Data_Fn} -#' @inheritParams Data_Fn +#' @param DataList list outputted from \code{make_data} +#' @inheritParams make_data #' @return Tagged list containing starting values for all fixed effects (parameters) and random effects (coefficients) #' \describe{ @@ -42,7 +42,7 @@ #' } #' @export -Param_Fn <- +make_parameters <- function( Version, DataList, RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0) ){ # Local function to make a random array diff --git a/R/zzz.R b/R/zzz.R index 12571e8..fc53d57 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -92,3 +92,26 @@ Summarize = function( ... ){ VAST::summarize_results( ... ) } +#' Copy of make_map +#' +#' Included for continuity when using old scripts +#' +#' Please use \code{?make_map} to see list of arguments and usage +#' @export +Make_Map = function( ... ){ + .Deprecated( new="make_map" ) + make_map( ... ) +} + +#' Copy of make_parameters +#' +#' Included for continuity when using old scripts +#' +#' Please use \code{?make_parameters} to see list of arguments and usage +#' @export +Param_Fn = function( ... ){ + .Deprecated( new="make_parameters" ) + make_parameters( ... ) +} + + diff --git a/man/Make_Map.Rd b/man/Make_Map.Rd new file mode 100644 index 0000000..02afe1f --- /dev/null +++ b/man/Make_Map.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/zzz.R +\name{Make_Map} +\alias{Make_Map} +\title{Copy of make_map} +\usage{ +Make_Map(...) +} +\description{ +Included for continuity when using old scripts +} +\details{ +Please use \code{?make_map} to see list of arguments and usage +} diff --git a/man/Param_Fn.Rd b/man/Param_Fn.Rd index dfdac80..77132c5 100644 --- a/man/Param_Fn.Rd +++ b/man/Param_Fn.Rd @@ -1,52 +1,14 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Param_Fn.R +% Please edit documentation in R/zzz.R \name{Param_Fn} \alias{Param_Fn} -\title{Calculate parameter inputs for TMB} +\title{Copy of make_parameters} \usage{ -Param_Fn(Version, DataList, RhoConfig = c(Beta1 = 0, Beta2 = 0, Epsilon1 - = 0, Epsilon2 = 0)) -} -\arguments{ -\item{DataList}{list outputted from \code{VAST::Data_Fn}} -} -\value{ -Tagged list containing starting values for all fixed effects (parameters) and random effects (coefficients) -\describe{ - \item{ln_H_input}{two parameters governing geometric anisotropy (rotation matrix H)} - \item{beta1_ct}{intercepts for encounter probability} - \item{gamma1_j}{effect of density covariates that are static over time on encounter prob} - \item{gamma1_ctp}{effect of density covariates that change over time on encounter prob} - \item{lambda1_k}{effect of catchability covariates on encounter prob} - \item{L1_z}{trimmed cholesky (i.e., parameters for square-root of covariance) for overdispersion in encounter prob} - \item{L_omega1_z}{trimmed cholesky pointwise variance in spatial variation in encounter prob} - \item{L_epsilon1_z}{trimmed cholesky pointwise variance in spatio-temporal variation in encounter prob} - \item{logkappa1}{governs decorrelation distance in encounter prob} - \item{Beta_mean1}{average intercept of encounter prob (used with RhoConfig options)} - \item{logsigmaB1}{SD for intercept of encounter prob (used with RhoConfig options)} - \item{Beta_rho1}{first-order autoregressive coefficient for intercept of encounter prob (used with RhoConfig options)} - \item{Epsilon_rho1}{first-order autoregressive coefficient for spatio-temporal variation of encounter prob (used with RhoConfig options)} - \item{eta1_vf}{overdispersion parameters (i.e., vessel or tow-level effects) on encounter prob} - \item{Omegainput1_sf}{Spatial variation in encounter prob} - \item{Epsiloninput1_sft}{Spatio-temporal variation in encounter prob} - \item{beta2_ct}{intercepts for positive catch rates} - \item{gamma2_j}{effect of density covariates that are static over time on positive catch rates} - \item{gamma2_ctp}{effect of density covariates that change over time on positive catch rates} - \item{lambda2_k}{effect of catchability covariates on positive catch rates} - \item{L2_z}{trimmed cholesky (i.e., parameters for square-root of covariance) for overdispersion in positive catch rates} - \item{L_omega2_z}{trimmed cholesky pointwise variance in spatial variation in positive catch rates} - \item{L_epsilon2_z}{trimmed cholesky pointwise variance in spatio-temporal variation in positive catch rates} - \item{logkappa2}{governs decorrelation distance in positive catch rates} - \item{Beta_mean2}{average intercept of positive catch rates (used with RhoConfig options)} - \item{logsigmaB2}{SD for intercept of positive catch rates (used with RhoConfig options)} - \item{Beta_rho2}{first-order autoregressive coefficient for intercept of positive catch rates (used with RhoConfig options)} - \item{Epsilon_rho2}{first-order autoregressive coefficient for spatio-temporal variation of positive catch rates (used with RhoConfig options)} - \item{eta2_vf}{overdispersion parameters (i.e., vessel or tow-level effects) on positive catch rates} - \item{Omegainput2_sf}{Spatial variation in positive catch rates} - \item{Epsiloninput2_sft}{Spatio-temporal variation in positive catch rates} - \item{logSigmaM}{variance parameters for positive catch ratesA} -} +Param_Fn(...) } \description{ -\code{Param_Fn} generates the \code{parameters} input for \code{TMB::MakeADFun} +Included for continuity when using old scripts +} +\details{ +Please use \code{?make_parameters} to see list of arguments and usage } diff --git a/man/make_parameters.Rd b/man/make_parameters.Rd new file mode 100644 index 0000000..b9c38fe --- /dev/null +++ b/man/make_parameters.Rd @@ -0,0 +1,56 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/make_parameters.R +\name{make_parameters} +\alias{make_parameters} +\title{Calculate parameter inputs for TMB} +\usage{ +make_parameters(Version, DataList, RhoConfig = c(Beta1 = 0, Beta2 = 0, + Epsilon1 = 0, Epsilon2 = 0)) +} +\arguments{ +\item{Version}{a version number; If missing, defaults to latest version using \code{FishStatsUtils::get_latest_version(package="VAST")}} + +\item{DataList}{list outputted from \code{make_data}} + +\item{RhoConfig}{vector of form c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0) specifying whether either intercepts (Beta1 and Beta2) or spatio-temporal variation (Epsilon1 and Epsilon2) is structured among time intervals (0: each year as fixed effect; 1: each year as random following IID distribution; 2: each year as random following a random walk; 3: constant among years as fixed effect; 4: each year as random following AR1 process); If missing, assumed to be zero for each element} +} +\value{ +Tagged list containing starting values for all fixed effects (parameters) and random effects (coefficients) +\describe{ + \item{ln_H_input}{two parameters governing geometric anisotropy (rotation matrix H)} + \item{beta1_ct}{intercepts for encounter probability} + \item{gamma1_j}{effect of density covariates that are static over time on encounter prob} + \item{gamma1_ctp}{effect of density covariates that change over time on encounter prob} + \item{lambda1_k}{effect of catchability covariates on encounter prob} + \item{L1_z}{trimmed cholesky (i.e., parameters for square-root of covariance) for overdispersion in encounter prob} + \item{L_omega1_z}{trimmed cholesky pointwise variance in spatial variation in encounter prob} + \item{L_epsilon1_z}{trimmed cholesky pointwise variance in spatio-temporal variation in encounter prob} + \item{logkappa1}{governs decorrelation distance in encounter prob} + \item{Beta_mean1}{average intercept of encounter prob (used with RhoConfig options)} + \item{logsigmaB1}{SD for intercept of encounter prob (used with RhoConfig options)} + \item{Beta_rho1}{first-order autoregressive coefficient for intercept of encounter prob (used with RhoConfig options)} + \item{Epsilon_rho1}{first-order autoregressive coefficient for spatio-temporal variation of encounter prob (used with RhoConfig options)} + \item{eta1_vf}{overdispersion parameters (i.e., vessel or tow-level effects) on encounter prob} + \item{Omegainput1_sf}{Spatial variation in encounter prob} + \item{Epsiloninput1_sft}{Spatio-temporal variation in encounter prob} + \item{beta2_ct}{intercepts for positive catch rates} + \item{gamma2_j}{effect of density covariates that are static over time on positive catch rates} + \item{gamma2_ctp}{effect of density covariates that change over time on positive catch rates} + \item{lambda2_k}{effect of catchability covariates on positive catch rates} + \item{L2_z}{trimmed cholesky (i.e., parameters for square-root of covariance) for overdispersion in positive catch rates} + \item{L_omega2_z}{trimmed cholesky pointwise variance in spatial variation in positive catch rates} + \item{L_epsilon2_z}{trimmed cholesky pointwise variance in spatio-temporal variation in positive catch rates} + \item{logkappa2}{governs decorrelation distance in positive catch rates} + \item{Beta_mean2}{average intercept of positive catch rates (used with RhoConfig options)} + \item{logsigmaB2}{SD for intercept of positive catch rates (used with RhoConfig options)} + \item{Beta_rho2}{first-order autoregressive coefficient for intercept of positive catch rates (used with RhoConfig options)} + \item{Epsilon_rho2}{first-order autoregressive coefficient for spatio-temporal variation of positive catch rates (used with RhoConfig options)} + \item{eta2_vf}{overdispersion parameters (i.e., vessel or tow-level effects) on positive catch rates} + \item{Omegainput2_sf}{Spatial variation in positive catch rates} + \item{Epsiloninput2_sft}{Spatio-temporal variation in positive catch rates} + \item{logSigmaM}{variance parameters for positive catch ratesA} +} +} +\description{ +\code{make_parameters} generates the \code{parameters} input for \code{TMB::MakeADFun} +} From fac3b23e33d8ee2b65b75a25fe4f93932b0c8123 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Wed, 24 Apr 2019 06:46:09 -0700 Subject: [PATCH 25/31] small changes to user-manual --- manual/VAST_model_structure.docx | Bin 82782 -> 86892 bytes manual/VAST_model_structure.pdf | Bin 662812 -> 665331 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z5k?8e3K^ve*9@b~wK1M7xD<5^;$X!-|7-YURB0R?qefT?88J9Bj0uK0W=d&mt|lZ! zr?H^Pl2G5oK`1O1TU#MWraQ<+!vlZ7o?%m`dyO5)DxkX*C+nrN&-s0riT~ z4>w;{&1eH@Kyn6-B`MxlX*d3-!mvWag~gMRRHwa8I1Tq~8qq{tN?jxen83@JxH1t) z?%L{y-_hG4Coej?63qYnsetqSwrKXlPM$}IL{1VytX|KcTTqlS2IYz~2AIIXaKa_d z7$<_n7lmIW$r=GraTK+Wt>HAD9S)4*ObAFAUbaF)(V-#+jQU4NfB1IeMG7Evsx;_d z1kAXIXGye@$U#Zqi_M68zyw~(KE|+|pc}O&(X*vakw;0zv#qTi#o&j3254z)E&i|1 z1BtV5q`jlF{YYcTk Date: Wed, 24 Apr 2019 07:27:11 -0700 Subject: [PATCH 26/31] adding packageDesription... ... output which saves github alpha-numeric code for the commit being used for VAST and FishStatsUtils --- R/make_model.R | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/R/make_model.R b/R/make_model.R index 2372538..72babf2 100644 --- a/R/make_model.R +++ b/R/make_model.R @@ -65,6 +65,10 @@ function( TmbData, Version, RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilo TmbData[["t_iz"]] = matrix( TmbData$t_i, ncol=1 ) } + # Save package version info + capture.output( packageDescription("VAST"), file=paste0(RunDir,"/packageDescription.txt") ) + capture.output( packageDescription("FishStatsUtils"), file=paste0(RunDir,"/packageDescription.txt"), append=TRUE ) + # Compile TMB software #dyn.unload( paste0(RunDir,"/",dynlib(TMB:::getUserDLL())) ) # random=Random, file.copy( from=paste0(TmbDir,"/",Version,".cpp"), to=paste0(RunDir,"/",Version,".cpp"), overwrite=FALSE) From dc09aa886b4dca4966a1132a1eb7acc600f9603d Mon Sep 17 00:00:00 2001 From: "James.Thorson" <1454196437@mil> Date: Wed, 8 May 2019 14:45:17 -0700 Subject: [PATCH 27/31] Update User Manual... ... to include description of fine_scale=TRUE option in V8.0.0 --- manual/VAST_model_structure.docx | Bin 86892 -> 96522 bytes manual/VAST_model_structure.pdf | Bin 665331 -> 678306 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/manual/VAST_model_structure.docx b/manual/VAST_model_structure.docx index 1f0c716becf656574716088637e7f4079861d80d..a11a1dd0500e90a6e773168a417d8a47b859713b 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zICZhw@w24MJ!TFw6YC>oW)`^;0S?_Vyf3c3`2Q>1T`l3x*YWv zoiH?5R3>&pAbdFj(=9%)hM+WebQpr8TwQ<>e2hdUb_~N9Leiv)Ll=fHD3@vs z1tfCiP>eS}FcdrjaOFsvH#m$!$Z=!v@vy@I#vL7wFa&pU;S>e4W&~ueLb>yaK+%bl z8~B$%Ntinxf+ToRPmnZ^a1y3@ViyNL}PoAaEyuqZp2Jy+DD%aml3^nBsOqqXfZ?0UD(#ZXYzkU@ETFG>LL1Hw-i2 zsdwDfG{H5RA&_wcUqvoMGBnp}hC;awA}~sl9A*Hgm|Zxoa;ovjFf{-#1uz~{suK@b$< zW^W8e7@U(tF&G8=iix@aqhO94SX`)=0rrA*U3lSu_0fDkp7Q`mlN^e57{hCjVo(l7 zkQ9t?bU_fI3}>zY4CIuTMyo~_U;vV@fiExyjPIr3C~&skbzFm`W4j!=GG^= Date: Thu, 9 May 2019 08:24:21 -0700 Subject: [PATCH 28/31] Fix backwards compatibility .... ... using updated FishStatsUtils but Version < 8.0.0 --- R/make_data.R | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/R/make_data.R b/R/make_data.R index 3a3b728..cec68f6 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -95,7 +95,7 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), if( !is.null(spatial_list$a_gl) ){ a_gl = a_xl = spatial_list$a_gl }else{ - stop("Must provide `a_gl` and/or `a_gl`") + stop("Must provide `a_gl` and/or `a_xl`") } }else{ a_gl = a_xl @@ -269,10 +269,15 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), # by default, add nothing as Z_xl if( Options2use['Calculate_Range']==FALSE ){ Z_gm = Z_xm = matrix(0, nrow=nrow(a_gl), ncol=ncol(a_gl) ) # Size so that it works for Version 3g-3j - }else{ - Z_xm = spatial_list$loc_x - Z_gm = spatial_list$loc_g + if( is.null(spatial_list) ){ + # Maintain backwards compatibility with Version < 8.0.0 + Z_xm = Z_gm = MeshList$loc_x + }else{ + # Improved interface used for Version >= 8.0.0 + Z_xm = spatial_list$loc_x + Z_gm = spatial_list$loc_g + } message( "Calculating range shift for stratum #1:",colnames(a_gl[1])) } From cf3a92b41ab6cd0dadfe64539c6d9602c1cee734 Mon Sep 17 00:00:00 2001 From: "James.Thorson" <1454196437@mil> Date: Thu, 9 May 2019 13:31:47 -0700 Subject: [PATCH 29/31] Improve make_data... ... to provide backwards compatibility and simplicity for V < 8.0.0 --- DESCRIPTION | 4 ++-- R/make_data.R | 55 +++++++++++++++++++++++++++++------------------- man/make_data.Rd | 53 ++++++++++++++++++++++------------------------ 3 files changed, 60 insertions(+), 52 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 40432e2..067704c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: VAST Type: Package Title: Vector-autoregressive spatio-temporal (VAST) model -Version: 2.1.0 -Date: 2019-03-01 +Version: 3.0.0 +Date: 2019-05-09 Authors@R: person("James","Thorson", email="James.Thorson@noaa.gov", role=c("aut","cre")) Maintainer: James Thorson Description: VAST is an R package for conducting spatio-temporal analysis diff --git a/R/make_data.R b/R/make_data.R index cec68f6..850e968 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -47,19 +47,20 @@ #' @param GridList, tagged list representing location information for the 2D AR1 grid hyperdistribution, i.e., from \code{SpatialDeltaGLMM::Spatial_Information_Fn} #' @param Method, character (either "Mesh" or "Grid") specifying hyperdistribution (Default="Mesh") #' @param PredTF_i OPTIONAL, whether each observation i is included in the likelihood (PredTF_i[i]=0) or in the predictive probability (PredTF_i[i]=1) -#' @param X_xtp array of dynamic (varying among time intervals) density covariates; if missing, assumed to not include covariates +#' @param X_gtp array of density covariates for each extrapolation-grid cell g, time t, and covariate p; if missing, assumed to not include covariates +#' @param X_itp array of density covariates for each extrapolation-grid cell i, time t, and covariate p #' @param Xconfig_zcp OPTIONAL, 3D array of settings for each dynamic density covariate, where the first dimension corresponds to 1st or 2nd linear predictors, second dimension corresponds to model category, and third dimension corresponds to each density covariate #' \describe{ -#' \item{Xconfig_zcp[z,c,p]=0}{\code{X_xtp[,,p]} has no effect on linear predictor z for category c} -#' \item{Xconfig_zcp[z,c,p]=1}{\code{X_xtp[,,p]} has a linear effect on linear predictor z for category c} -#' \item{Xconfig_zcp[z,c,p]=2}{\code{X_xtp[,,p]} has a spatially varying, zero-centered linear effect on linear predictor z for category c} -#' \item{Xconfig_zcp[z,c,p]=3}{\code{X_xtp[,,p]} has a spatially varying linear effect on linear predictor z for category c} +#' \item{Xconfig_zcp[z,c,p]=0}{\code{X_itp[,,p]} has no effect on linear predictor z for category c} +#' \item{Xconfig_zcp[z,c,p]=1}{\code{X_itp[,,p]} has a linear effect on linear predictor z for category c} +#' \item{Xconfig_zcp[z,c,p]=2}{\code{X_itp[,,p]} has a spatially varying, zero-centered linear effect on linear predictor z for category c} +#' \item{Xconfig_zcp[z,c,p]=3}{\code{X_itp[,,p]} has a spatially varying linear effect on linear predictor z for category c} #' } #' @param Q_ik matrix of catchability covariates (e.g., measured variables affecting catch rates but not caused by variation in species density) for each observation i #' @param Aniso whether to assume isotropy (Aniso=0) or geometric anisotropy (Aniso=1) #' @param F_ct matrix of fishing mortality for each category c and year t (only feasible when using a Poisson-link delta model and specifying temporal structure on intercepts, and mainly interpretable when species interactions via VamConfig) #' @param t_yz matrix specifying combination of levels of \code{t_iz} to use when calculating different indices of abundance or range shifts -#' @param Options a vector of form c('SD_site_logdensity'=0,'Calculate_Range'=0,'Calculate_effective_area'=0,'Calculate_Cov_SE'=0,'Calculate_Synchrony'=0,'Calculate_proportion'=0), where Calculate_Range=1 turns on calculation of center of gravity, and Calculate_effective_area=1 turns on calculation of effective area occupied +#' @param Options a vector of form c('SD_site_logdensity'=FALSE,'Calculate_Range'=FALSE,'Calculate_effective_area'=FALSE,'Calculate_Cov_SE'=FALSE,'Calculate_Synchrony'=FALSE,'Calculate_proportion'=FALSE), where Calculate_Range=1 turns on calculation of center of gravity, and Calculate_effective_area=1 turns on calculation of effective area occupied #' @param yearbounds_zz matrix with two columns, giving first and last years for defining one or more periods (rows) used to calculate changes in synchrony over time (only used if \code{Options['Calculate_Synchrony']=1}) #' @param CheckForErrors whether to check for errors in input (NOTE: when CheckForErrors=TRUE, the function will throw an error if it detects a problem with inputs. However, failing to throw an error is no guaruntee that the inputs are all correct) @@ -70,13 +71,33 @@ make_data <- function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), FieldConfig, OverdispersionConfig=c("eta1"=0,"eta2"=0), ObsModel_ez=c("PosDist"=1,"Link"=0), RhoConfig=c("Beta1"=0,"Beta2"=0,"Epsilon1"=0,"Epsilon2"=0), VamConfig=c("Method"=0,"Rank"=0,"Timing"=0), - spatial_list=NULL, a_xl=NULL, MeshList=spatial_list$MeshList, GridList=spatial_list$GridList, - Method=spatial_list$Method, s_i=spatial_list$knot_i-1, Aniso=TRUE, PredTF_i=rep(0,length(b_i)), - X_xj=NULL, X_xtp=NULL, Xconfig_zcp=NULL, X_gtp=NULL, X_itp=NULL, + spatial_list=NULL, Aniso=TRUE, PredTF_i=rep(0,length(b_i)), + Xconfig_zcp=NULL, X_gtp=NULL, X_itp=NULL, Q_ik=NULL, Network_sz=NULL, F_ct=NULL, F_init=1, t_yz=NULL, CheckForErrors=TRUE, yearbounds_zz=NULL, - Options=c('SD_site_logdensity'=FALSE,'Calculate_Range'=FALSE,'Calculate_effective_area'=FALSE,'Calculate_Cov_SE'=FALSE), - Expansion_cz=NULL, Version=FishStatsUtils::get_latest_version(package="VAST") ){ + Options=c(), Expansion_cz=NULL, + Version=FishStatsUtils::get_latest_version(package="VAST"), ... ){ + + # Deprecated inputs for backwards compatibility in transition from Version < 8.0.0 to >= 8.0.0 + deprecated_inputs = list( ... ) + X_xtp = deprecated_inputs[["X_xtp"]] + if( is.null(spatial_list) ){ + warning("Consider changing use of `make_data` to include `spatial_list` as input") + a_xl = a_gl = deprecated_inputs[["a_xl"]] + MeshList = deprecated_inputs[["MeshList"]] + GridList = deprecated_inputs[["GridList"]] + Method = deprecated_inputs[["Method"]] + s_i = deprecated_inputs[["s_i"]] + }else{ + MeshList = spatial_list[["MeshList"]] + GridList = spatial_list[["GridList"]] + Method = spatial_list[["Method"]] + a_xl = a_gl = spatial_list[["a_gl"]] + s_i = spatial_list[["knot_i"]] - 1 + } + + # Deprecated inputs for backwards compatibility with earlier versions + X_xj = deprecated_inputs[["X_xj"]] # Specify default values for `Options` Options2use = c('SD_site_density'=FALSE, 'SD_site_logdensity'=FALSE, 'Calculate_Range'=FALSE, 'SD_observation_density'=FALSE, 'Calculate_effective_area'=FALSE, @@ -91,16 +112,6 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), } # Check for backwards-compatibility issues - if( is.null(a_xl) ){ - if( !is.null(spatial_list$a_gl) ){ - a_gl = a_xl = spatial_list$a_gl - }else{ - stop("Must provide `a_gl` and/or `a_xl`") - } - }else{ - a_gl = a_xl - warning("Please consider using input `a_gl`; `a_xl` is still available for backwards compatiblity but only `a_gl` will be used") - } if( FishStatsUtils::convert_version_name(Version) >= FishStatsUtils::convert_version_name("VAST_v8_0_0") ){ if( !is.null(X_xtp) ){ stop("`X_xtp` is not used in version >= 8.0.0") @@ -591,7 +602,7 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_x"=n_x, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_xm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=FieldConfig_input, "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "Xconfig_zcp"=Xconfig_zcp, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_xl"=a_gl, "X_xtp"=X_xtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_xm"=Z_xm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2 ) } if(Version%in%c("VAST_v8_0_0")){ - Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_g"=n_g, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_gm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=FieldConfig_input, "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "Xconfig_zcp"=Xconfig_zcp, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "s_i"=s_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_gl"=a_gl, "X_itp"=X_itp, "X_gtp"=X_gtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_gm"=Z_gm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2, "Ais_ij"=cbind(spatial_list$A_is@i,spatial_list$A_is@j), "Ais_x"=spatial_list$A_is@x, "Ags_ij"=cbind(spatial_list$A_gs@i,spatial_list$A_gs@j), "Ags_x"=spatial_list$A_gs@x ) + Return = list( "n_i"=n_i, "n_s"=c(MeshList$anisotropic_spde$n.spde,n_x,n_x)[Options_vec['Method']+1], "n_g"=n_g, "n_t"=n_t, "n_c"=n_c, "n_e"=n_e, "n_p"=n_p, "n_v"=n_v, "n_l"=n_l, "n_m"=ncol(Z_gm), "Options_list"=list("Options_vec"=Options_vec,"Options"=Options2use,"yearbounds_zz"=yearbounds_zz,"Expansion_cz"=Expansion_cz), "FieldConfig"=FieldConfig_input, "RhoConfig"=RhoConfig, "OverdispersionConfig"=OverdispersionConfig_input, "ObsModel_ez"=ObsModel_ez, "VamConfig"=VamConfig, "Xconfig_zcp"=Xconfig_zcp, "include_data"=TRUE, "b_i"=b_i, "a_i"=a_i, "c_iz"=c_iz, "e_i"=e_i, "t_iz"=tprime_iz, "v_i"=match(v_i,sort(unique(v_i)))-1, "PredTF_i"=PredTF_i, "a_gl"=a_gl, "X_itp"=X_itp, "X_gtp"=X_gtp, "Q_ik"=Q_ik, "t_yz"=t_yz, "Z_gm"=Z_gm, "F_ct"=F_ct, "parent_s"=Network_sz[,'parent_s']-1, "child_s"=Network_sz[,'child_s']-1, "dist_s"=Network_sz[,'dist_s'], "spde"=list(), "spde_aniso"=list(), "M0"=GridList$M0, "M1"=GridList$M1, "M2"=GridList$M2, "Ais_ij"=cbind(spatial_list$A_is@i,spatial_list$A_is@j), "Ais_x"=spatial_list$A_is@x, "Ags_ij"=cbind(spatial_list$A_gs@i,spatial_list$A_gs@j), "Ags_x"=spatial_list$A_gs@x ) } if( is.null(Return) ) stop("`Version` provided does not match the list of possible values") if( "spde" %in% names(Return) ) Return[['spde']] = MeshList$isotropic_spde$param.inla[c("M0","M1","M2")] diff --git a/man/make_data.Rd b/man/make_data.Rd index 5b743eb..5ba6044 100644 --- a/man/make_data.Rd +++ b/man/make_data.Rd @@ -8,17 +8,12 @@ make_data(b_i, a_i, c_iz, t_iz, e_i = c_iz[, 1], v_i = rep(0, length(b_i)), FieldConfig, OverdispersionConfig = c(eta1 = 0, eta2 = 0), ObsModel_ez = c(PosDist = 1, Link = 0), RhoConfig = c(Beta1 = 0, Beta2 = 0, Epsilon1 = 0, Epsilon2 = 0), VamConfig = c(Method = 0, Rank - = 0, Timing = 0), spatial_list = NULL, a_xl = NULL, - MeshList = spatial_list$MeshList, GridList = spatial_list$GridList, - Method = spatial_list$Method, s_i = spatial_list$knot_i - 1, - Aniso = TRUE, PredTF_i = rep(0, length(b_i)), X_xj = NULL, - X_xtp = NULL, Xconfig_zcp = NULL, X_gtp = NULL, X_itp = NULL, - Q_ik = NULL, Network_sz = NULL, F_ct = NULL, F_init = 1, - t_yz = NULL, CheckForErrors = TRUE, yearbounds_zz = NULL, - Options = c(SD_site_logdensity = FALSE, Calculate_Range = FALSE, - Calculate_effective_area = FALSE, Calculate_Cov_SE = FALSE), - Expansion_cz = NULL, - Version = FishStatsUtils::get_latest_version(package = "VAST")) + = 0, Timing = 0), spatial_list = NULL, Aniso = TRUE, + PredTF_i = rep(0, length(b_i)), Xconfig_zcp = NULL, X_gtp = NULL, + X_itp = NULL, Q_ik = NULL, Network_sz = NULL, F_ct = NULL, + F_init = 1, t_yz = NULL, CheckForErrors = TRUE, + yearbounds_zz = NULL, Options = c(), Expansion_cz = NULL, + Version = FishStatsUtils::get_latest_version(package = "VAST"), ...) } \arguments{ \item{b_i}{Sampled biomass for each observation i} @@ -70,30 +65,22 @@ make_data(b_i, a_i, c_iz, t_iz, e_i = c_iz[, 1], v_i = rep(0, \item{spatial_list}{tagged list of locatoinal information from , i.e., from \code{FishStatsUtils::make_spatial_info}} -\item{a_xl}{Area associated with each knot} - -\item{MeshList, }{tagged list representing location information for the SPDE mesh hyperdistribution, i.e., from \code{SpatialDeltaGLMM::Spatial_Information_Fn}} - -\item{GridList, }{tagged list representing location information for the 2D AR1 grid hyperdistribution, i.e., from \code{SpatialDeltaGLMM::Spatial_Information_Fn}} - -\item{Method, }{character (either "Mesh" or "Grid") specifying hyperdistribution (Default="Mesh")} - -\item{s_i}{Spatial knot (e.g., grid cell) for each observation i} - \item{Aniso}{whether to assume isotropy (Aniso=0) or geometric anisotropy (Aniso=1)} \item{PredTF_i}{OPTIONAL, whether each observation i is included in the likelihood (PredTF_i[i]=0) or in the predictive probability (PredTF_i[i]=1)} -\item{X_xtp}{array of dynamic (varying among time intervals) density covariates; if missing, assumed to not include covariates} - \item{Xconfig_zcp}{OPTIONAL, 3D array of settings for each dynamic density covariate, where the first dimension corresponds to 1st or 2nd linear predictors, second dimension corresponds to model category, and third dimension corresponds to each density covariate \describe{ - \item{Xconfig_zcp[z,c,p]=0}{\code{X_xtp[,,p]} has no effect on linear predictor z for category c} - \item{Xconfig_zcp[z,c,p]=1}{\code{X_xtp[,,p]} has a linear effect on linear predictor z for category c} - \item{Xconfig_zcp[z,c,p]=2}{\code{X_xtp[,,p]} has a spatially varying, zero-centered linear effect on linear predictor z for category c} - \item{Xconfig_zcp[z,c,p]=3}{\code{X_xtp[,,p]} has a spatially varying linear effect on linear predictor z for category c} + \item{Xconfig_zcp[z,c,p]=0}{\code{X_itp[,,p]} has no effect on linear predictor z for category c} + \item{Xconfig_zcp[z,c,p]=1}{\code{X_itp[,,p]} has a linear effect on linear predictor z for category c} + \item{Xconfig_zcp[z,c,p]=2}{\code{X_itp[,,p]} has a spatially varying, zero-centered linear effect on linear predictor z for category c} + \item{Xconfig_zcp[z,c,p]=3}{\code{X_itp[,,p]} has a spatially varying linear effect on linear predictor z for category c} }} +\item{X_gtp}{array of density covariates for each extrapolation-grid cell g, time t, and covariate p; if missing, assumed to not include covariates} + +\item{X_itp}{array of density covariates for each extrapolation-grid cell i, time t, and covariate p} + \item{Q_ik}{matrix of catchability covariates (e.g., measured variables affecting catch rates but not caused by variation in species density) for each observation i} \item{F_ct}{matrix of fishing mortality for each category c and year t (only feasible when using a Poisson-link delta model and specifying temporal structure on intercepts, and mainly interpretable when species interactions via VamConfig)} @@ -104,9 +91,19 @@ make_data(b_i, a_i, c_iz, t_iz, e_i = c_iz[, 1], v_i = rep(0, \item{yearbounds_zz}{matrix with two columns, giving first and last years for defining one or more periods (rows) used to calculate changes in synchrony over time (only used if \code{Options['Calculate_Synchrony']=1})} -\item{Options}{a vector of form c('SD_site_logdensity'=0,'Calculate_Range'=0,'Calculate_effective_area'=0,'Calculate_Cov_SE'=0,'Calculate_Synchrony'=0,'Calculate_proportion'=0), where Calculate_Range=1 turns on calculation of center of gravity, and Calculate_effective_area=1 turns on calculation of effective area occupied} +\item{Options}{a vector of form c('SD_site_logdensity'=FALSE,'Calculate_Range'=FALSE,'Calculate_effective_area'=FALSE,'Calculate_Cov_SE'=FALSE,'Calculate_Synchrony'=FALSE,'Calculate_proportion'=FALSE), where Calculate_Range=1 turns on calculation of center of gravity, and Calculate_effective_area=1 turns on calculation of effective area occupied} \item{Version}{a version number; If missing, defaults to latest version using \code{FishStatsUtils::get_latest_version(package="VAST")}} + +\item{s_i}{Spatial knot (e.g., grid cell) for each observation i} + +\item{a_xl}{Area associated with each knot} + +\item{MeshList, }{tagged list representing location information for the SPDE mesh hyperdistribution, i.e., from \code{SpatialDeltaGLMM::Spatial_Information_Fn}} + +\item{GridList, }{tagged list representing location information for the 2D AR1 grid hyperdistribution, i.e., from \code{SpatialDeltaGLMM::Spatial_Information_Fn}} + +\item{Method, }{character (either "Mesh" or "Grid") specifying hyperdistribution (Default="Mesh")} } \value{ Tagged list containing inputs to function \code{VAST::Build_TMB_Fn()} From a1470a81b847c000137a058cb2687a4914268d04 Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Thu, 9 May 2019 19:25:52 -0700 Subject: [PATCH 30/31] small improvement in informative errors... ... related to covariates and update to V8.0.0 --- R/make_data.R | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/R/make_data.R b/R/make_data.R index 850e968..e30ce26 100644 --- a/R/make_data.R +++ b/R/make_data.R @@ -114,11 +114,13 @@ function( b_i, a_i, c_iz, t_iz, e_i=c_iz[,1], v_i=rep(0,length(b_i)), # Check for backwards-compatibility issues if( FishStatsUtils::convert_version_name(Version) >= FishStatsUtils::convert_version_name("VAST_v8_0_0") ){ if( !is.null(X_xtp) ){ - stop("`X_xtp` is not used in version >= 8.0.0") + stop("`X_xtp` is not used in version >= 8.0.0. If you'd like to specify covariates using input `X_xtp` please use `Version='VAST_v7_0_0'`") } } if( FishStatsUtils::convert_version_name(Version) <= FishStatsUtils::convert_version_name("VAST_v7_0_0") ){ - if( !is.null(X_gtp) | !is.null(X_itp) ) stop("`X_gtp` and `X_itp` are not used in version <= 7.0.0") + if( !is.null(X_gtp) | !is.null(X_itp) ){ + stop("`X_gtp` and `X_itp` are not used in version <= 7.0.0. If you'd like to specify covariates using input `X_gtp` and `X_itp` please use `Version='VAST_v8_0_0'` or higher") + } } # Adds intercept defaults to FieldConfig if missing From c4dcb13e185e80e2e289e7e46a1cfe2a3787ca8c Mon Sep 17 00:00:00 2001 From: "James.Thorson" Date: Fri, 10 May 2019 06:51:28 -0700 Subject: [PATCH 31/31] Updating dependency... ... on FishStatsUtils to install version 2.0.0 if using earlier version --- R/zzz.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/zzz.R b/R/zzz.R index fc53d57..f03e3da 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -18,9 +18,9 @@ # Load `FishStatsUtils` via .onAttach because importFrom wasn't working # Also requries moving FishStatsUtils to SUGGESTS, so that it doesn't isntall main branch - if( !"FishStatsUtils" %in% utils::installed.packages()[,1] || utils::packageVersion("FishStatsUtils") < numeric_version("1.1.0") ){ - packageStartupMessage("Updating package FishStatsUtils because previously using version < 1.1.0") - devtools::install_github("james-thorson/FishStatsUtils", ref="development") + if( !"FishStatsUtils" %in% utils::installed.packages()[,1] || utils::packageVersion("FishStatsUtils") < numeric_version("2.0.0") ){ + packageStartupMessage("Updating package FishStatsUtils because previously using version < 2.0.0") + devtools::install_github("james-thorson/FishStatsUtils", ref="2.0.0") } packageStartupMessage( "Loading package `FishStatsUtils`" ) library(FishStatsUtils)

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