From 44b10f784737cea130d947318c33dde1dea0d304 Mon Sep 17 00:00:00 2001 From: Jim Thorson <50178738+James-Thorson-NOAA@users.noreply.github.com> Date: Sun, 17 Dec 2023 16:58:49 -0800 Subject: [PATCH] adding Archetypes vignette --- .Rbuildignore | 5 + .github/.gitignore | 1 + .github/workflows/pkgdown.yaml | 48 ++++ .gitignore | 1 + DESCRIPTION | 13 +- NAMESPACE | 2 + R/Fit_model.R | 2 +- R/fit_tmb.R | 208 ++++++++++++++ R/list_parameters.R | 28 ++ _pkgdown.yml | 4 + man/FishBase_and_Morphometrics.Rd | 6 +- man/Update_prediction.Rd | 52 ---- man/fit_tmb.Rd | 92 ++++++ man/list_parameters.Rd | 17 ++ scratch/build_pkgdown_website.R | 39 +++ vignettes/Archetypes.Rmd | 113 ++++++++ vignettes/Archetypes.html | 456 ++++++++++++++++++++++++++++++ vignettes/Archetypes.pdf | Bin 0 -> 418856 bytes 18 files changed, 1025 insertions(+), 62 deletions(-) create mode 100644 .github/.gitignore create mode 100644 .github/workflows/pkgdown.yaml create mode 100644 R/fit_tmb.R create mode 100644 R/list_parameters.R create mode 100644 _pkgdown.yml delete mode 100644 man/Update_prediction.Rd create mode 100644 man/fit_tmb.Rd create mode 100644 man/list_parameters.Rd create mode 100644 scratch/build_pkgdown_website.R create mode 100644 vignettes/Archetypes.Rmd create mode 100644 vignettes/Archetypes.html create mode 100644 vignettes/Archetypes.pdf diff --git a/.Rbuildignore b/.Rbuildignore index 918c73c..eb3e566 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -3,3 +3,8 @@ cran-comments.md ^\.travis\.yml$ ^.*\.Rproj$ ^\.Rproj\.user$ +^scratch$ +^_pkgdown\.yml$ +^docs$ +^pkgdown$ +^\.github$ diff --git a/.github/.gitignore b/.github/.gitignore new file mode 100644 index 0000000..2d19fc7 --- /dev/null +++ b/.github/.gitignore @@ -0,0 +1 @@ +*.html diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml new file mode 100644 index 0000000..ed7650c --- /dev/null +++ b/.github/workflows/pkgdown.yaml @@ -0,0 +1,48 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + release: + types: [published] + workflow_dispatch: + +name: pkgdown + +jobs: + pkgdown: + runs-on: ubuntu-latest + # Only restrict concurrency for non-PR jobs + concurrency: + group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + permissions: + contents: write + steps: + - uses: actions/checkout@v3 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::pkgdown, local::. + needs: website + + - name: Build site + run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE) + shell: Rscript {0} + + - name: Deploy to GitHub pages 🚀 + if: github.event_name != 'pull_request' + uses: JamesIves/github-pages-deploy-action@v4.4.1 + with: + clean: false + branch: gh-pages + folder: docs diff --git a/.gitignore b/.gitignore index 1c271bf..1e77388 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,4 @@ *.Rproj *.Rproj.user .Rproj.user +docs diff --git a/DESCRIPTION b/DESCRIPTION index d1c2232..a9c1788 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: FishLife Type: Package Title: Predict Life History Parameters For Any Fish -Version: 3.0.0 -Date: 2023-01-31 +Version: 3.1.0 +Date: 2023-12-17 Authors@R: person("James","Thorson", email="James.Thorson@noaa.gov", role=c("aut","cre")) Maintainer: James Thorson Description: Estimate trade-offs among life history parameters using phylogenetic structural equation models, @@ -15,8 +15,6 @@ Imports: mvtnorm, shape, rfishbase, - ThorsonUtilities, - TMBhelper, sem, ape Depends: @@ -25,15 +23,14 @@ Suggests: TMB, testthat, knitr, - rmarkdown + rmarkdown, + archetypes Remotes: ropensci/rfishbase@fb-21.06, - james-thorson/utilities, - kaskr/TMB_contrib_R/TMBhelper License: GPL-3 LazyData: yes BuildVignettes: yes RoxygenNote: 7.2.3 -URL: http://github.com/James-Thorson-NOAA/FishLife +URL: http://github.com/James-Thorson-NOAA/FishLife, https://james-thorson-noaa.github.io/FishLife/ BugReports: http://github.com/James-Thorson-NOAA/FishLife/issues VignetteBuilder: knitr diff --git a/NAMESPACE b/NAMESPACE index dccafd3..43d4c17 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -12,6 +12,8 @@ export(Plot_trait) export(Predictive_distribution) export(Resize_estimates) export(Search_species) +export(fit_tmb) +export(list_parameters) export(update_prediction) importFrom(grDevices,rainbow) importFrom(grDevices,rgb) diff --git a/R/Fit_model.R b/R/Fit_model.R index 6868c5b..db775fb 100644 --- a/R/Fit_model.R +++ b/R/Fit_model.R @@ -718,7 +718,7 @@ function( text = NULL, # Optimize # , startpar=opt$par[-grep("alpha",names(opt$par))] # JointPrecision is used below, and is too big to invert whole; must have getReportCovariance=TRUE to get JointPrecision - Opt = TMBhelper::fit_tmb( obj = Obj, + Opt = fit_tmb( obj = Obj, savedir = RunDir, getJointPrecision = TRUE, getReportCovariance = TRUE, diff --git a/R/fit_tmb.R b/R/fit_tmb.R new file mode 100644 index 0000000..53ac41b --- /dev/null +++ b/R/fit_tmb.R @@ -0,0 +1,208 @@ + +#' Optimize a TMB model +#' +#' \code{fit_tmb} runs a TMB model and generates standard diagnostics +#' +#' @inheritParams stats::nlminb +#' @inheritParams TMB::sdreport +#' @param obj The compiled TMB object +#' @param startpar Starting values for fixed effects (default NULL uses \code{obj$par}) +#' @param control A list of control parameters. For details see \code{\link[stats]{nlminb}} +#' @param getsd Boolean indicating whether to run standard error calculation; see \code{\link[TMB]{sdreport}} for details +#' @param bias.correct Boolean indicating whether to do epsilon bias-correction; +#' see \code{\link[TMB]{sdreport}} and \code{\link[TMBhelper]{fit_tmb}}for details +#' @param bias.correct.control tagged list of options for epsilon bias-correction, +#' where \code{vars_to_correct} is a character-vector of ADREPORT variables that should be bias-corrected +#' @param savedir directory to save results (if \code{savedir=NULL}, then results aren't saved) +#' @param loopnum number of times to re-start optimization (where \code{loopnum=3} +#' sometimes achieves a lower final gradient than \code{loopnum=1}) +#' @param newtonsteps Integer specifying the number of extra newton steps to take +#' after optimization (alternative to \code{loopnum}). +#' Each newtonstep requires calculating the Hessian matrix and is therefore slow. +#' But for well-behaved models, each Newton step will typically +#' decrease the maximum gradient of the loglikelihood with respect to each fixed effect, +#' and therefore this option can be used to achieve an arbitrarily low final gradient +#' given sufficient time for well-behaved models. However, this option will also +#' perform strangely or have unexpected consequences for poorly-behaved models, e.g., +#' when fixed effects are at upper or lower bounds. +#' @param n sample sizes (if \code{n!=Inf} then \code{n} is used to calculate BIC and AICc) +#' @param getHessian return Hessian for usage in later code +#' @param quiet Boolean whether to print additional messages results to terminal +#' @param start_time_elapsed how much time has elapsed prior to calling fit_tmb, +#' for use, e.g., when calling \code{fit_tmb} multiple times in sequence, +#' where \code{start_time_elapsed = opt_previous$time_for_run} +#' @param ... list of settings to pass to \code{\link[TMB]{sdreport}} +#' +#' @return the standard output from \code{\link[stats]{nlminb}}, except with additional diagnostics and timing info, +#' and a new slot containing the output from \code{\link[TMB]{sdreport}} +#' +#' @references For more details see \url{https://doi.org/10.1016/j.fishres.2015.11.016} +#' @export +fit_tmb <- +function( obj, + fn = obj$fn, + gr = obj$gr, + startpar = NULL, + lower = -Inf, + upper = Inf, + getsd = TRUE, + control = list(eval.max = 1e4, iter.max = 1e4, trace = 1), + bias.correct = FALSE, + bias.correct.control = list(sd = FALSE, split = NULL, nsplit = NULL, vars_to_correct = NULL), + savedir = NULL, + loopnum = 2, + newtonsteps = 0, + n = Inf, + getReportCovariance = FALSE, + getJointPrecision = FALSE, + getHessian = FALSE, + quiet = FALSE, + start_time_elapsed = as.difftime("0:0:0"), + ... ){ + + # Defaults + if(is.null(startpar)) startpar = obj$par + + # Check for issues + if( bias.correct==TRUE & is.null(obj$env$random) ){ + message( "No random effects detected in TMB model, so overriding user input to `TMBhelper::fit_tmb` to instead specify `bias.correct=FALSE`") + bias.correct = FALSE + } + if( getReportCovariance==FALSE ){ + message( "Note that `getReportCovariance=FALSE` causes an error in `TMB::sdreport` when no ADREPORTed variables are present") + } + + # Check for issues + List = list(...) + #if( !is.null(List$jointJointPrecision) ){ + # if( getJointPrecision==FALSE & getReportCovariance==TRUE ){ + # stop("Some versions of TMB appear to throw an error when `getJointPrecision=TRUE` and `getReportCovariance=FALSE`") + # } + #} + + # Local function -- combine two lists + combine_lists = function( default, input ){ + output = default + for( i in seq_along(input) ){ + if( names(input)[i] %in% names(default) ){ + output[[names(input)[i]]] = input[[i]] + }else{ + output = c( output, input[i] ) + } + } + return( output ) + } + + # Replace defaults for `BS.control` with provided values (if any) + BS.control = list(sd=FALSE, split=NULL, nsplit=NULL, vars_to_correct=NULL) + BS.control = combine_lists( default=BS.control, input=bias.correct.control ) + + # Replace defaults for `control` with provided values if any + nlminb.control = list(eval.max=1e4, iter.max=1e4, trace=0) + nlminb.control = combine_lists( default=nlminb.control, input=control ) + + # Run first time + start_time = Sys.time() + parameter_estimates = nlminb( start=startpar, objective=fn, gradient=gr, control=nlminb.control, lower=lower, upper=upper ) + + # Re-run to further decrease final gradient + for( i in seq(2,loopnum,length=max(0,loopnum-1)) ){ + Temp = parameter_estimates[c('iterations','evaluations')] + parameter_estimates = nlminb( start=parameter_estimates$par, objective=fn, gradient=gr, control=nlminb.control, lower=lower, upper=upper ) + parameter_estimates[['iterations']] = parameter_estimates[['iterations']] + Temp[['iterations']] + parameter_estimates[['evaluations']] = parameter_estimates[['evaluations']] + Temp[['evaluations']] + } + + ## Run some Newton steps + for(i in seq_len(newtonsteps)) { + g = as.numeric( gr(parameter_estimates$par) ) + h = optimHess(parameter_estimates$par, fn=fn, gr=gr) + parameter_estimates$par = parameter_estimates$par - solve(h, g) + parameter_estimates$objective = fn(parameter_estimates$par) + } + + # Exclude difficult-to-interpret messages + parameter_estimates = parameter_estimates[c('par','objective','iterations','evaluations')] + + # Add diagnostics + parameter_estimates[["time_for_MLE"]] = Sys.time() - start_time + parameter_estimates[["max_gradient"]] = max(abs(gr(parameter_estimates$par))) + parameter_estimates[["Convergence_check"]] = ifelse( parameter_estimates[["max_gradient"]]<0.0001, "There is no evidence that the model is not converged", "The model is likely not converged" ) + parameter_estimates[["number_of_coefficients"]] = c("Total"=length(unlist(obj$env$parameters)), "Fixed"=length(startpar), "Random"=length(unlist(obj$env$parameters))-length(startpar) ) + parameter_estimates[["AIC"]] = TMBhelper::TMBAIC( opt=parameter_estimates ) + if( n!=Inf ){ + parameter_estimates[["AICc"]] = TMBhelper::TMBAIC( opt=parameter_estimates, n=n ) + parameter_estimates[["BIC"]] = TMBhelper::TMBAIC( opt=parameter_estimates, p=log(n) ) + } + parameter_estimates[["diagnostics"]] = data.frame( "Param"=names(startpar), "starting_value"=startpar, "Lower"=lower, "MLE"=parameter_estimates$par, "Upper"=upper, "final_gradient"=as.vector(gr(parameter_estimates$par)) ) + + # Get standard deviations + if(getsd==TRUE){ + # Compute hessian + sd_time = Sys.time() + h = optimHess(parameter_estimates$par, fn=fn, gr=gr) + # Check for problems + if( is.character(try(chol(h),silent=TRUE)) ){ + warning("Hessian is not positive definite, so standard errors are not available") + if( !is.null(savedir) ){ + capture.output( parameter_estimates, file=file.path(savedir,"parameter_estimates.txt")) + } + return( list("opt"=parameter_estimates, "h"=h) ) + } + # Compute standard errors + if( bias.correct==FALSE | is.null(BS.control[["vars_to_correct"]]) ){ + if( !is.null(BS.control[["nsplit"]]) ) { + if( BS.control[["nsplit"]] == 1 ) BS.control[["nsplit"]] = NULL + } + parameter_estimates[["SD"]] = TMB::sdreport( obj=obj, par.fixed=parameter_estimates$par, hessian.fixed=h, bias.correct=bias.correct, + bias.correct.control=BS.control[c("sd","split","nsplit")], getReportCovariance=getReportCovariance, + getJointPrecision=getJointPrecision, ... ) + }else{ + if( "ADreportIndex" %in% names(obj$env) ){ + Which = as.vector(unlist( obj$env$ADreportIndex()[ BS.control[["vars_to_correct"]] ] )) + }else{ + # Run first time to get indices + parameter_estimates[["SD"]] = TMB::sdreport( obj=obj, par.fixed=parameter_estimates$par, hessian.fixed=h, bias.correct=FALSE, + getReportCovariance=FALSE, getJointPrecision=FALSE, ... ) + # Determine indices + Which = which( rownames(summary(parameter_estimates[["SD"]],"report")) %in% BS.control[["vars_to_correct"]] ) + } + # Split up indices + if( !is.null(BS.control[["nsplit"]]) && BS.control[["nsplit"]]>1 ){ + Which = split( Which, cut(seq_along(Which), BS.control[["nsplit"]]) ) + } + Which = Which[sapply(Which,FUN=length)>0] + if(length(Which)==0) Which = NULL + # Repeat SD with indexing + message( paste0("Bias correcting ", length(Which), " derived quantities") ) + parameter_estimates[["SD"]] = TMB::sdreport( obj=obj, par.fixed=parameter_estimates$par, hessian.fixed=h, bias.correct=TRUE, + bias.correct.control=list(sd=BS.control[["sd"]], split=Which, nsplit=NULL), getReportCovariance=getReportCovariance, + getJointPrecision=getJointPrecision, ... ) + } + # Update + parameter_estimates[["Convergence_check"]] = ifelse( parameter_estimates$SD$pdHess==TRUE, parameter_estimates[["Convergence_check"]], "The model is definitely not converged" ) + parameter_estimates[["time_for_sdreport"]] = Sys.time() - sd_time + # Add hessian to parameter_estimates + if( getHessian==TRUE ){ + parameter_estimates[["hessian"]] = h + } + } + parameter_estimates[["time_for_run"]] = Sys.time() - start_time + start_time_elapsed + + # Save results + if( !is.null(savedir) ){ + save( parameter_estimates, file=file.path(savedir,"parameter_estimates.RData")) + capture.output( parameter_estimates, file=file.path(savedir,"parameter_estimates.txt")) + } + + # Print warning to screen + if( quiet==FALSE & parameter_estimates[["Convergence_check"]] != "There is no evidence that the model is not converged" ){ + message( "#########################" ) + message( parameter_estimates[["Convergence_check"]] ) + message( "#########################" ) + } + + # Return stuff + return( parameter_estimates ) +} + diff --git a/R/list_parameters.R b/R/list_parameters.R new file mode 100644 index 0000000..8b69adc --- /dev/null +++ b/R/list_parameters.R @@ -0,0 +1,28 @@ + + +#' List fixed and random effects +#' +#' \code{list_parameters} lists all fixed and random effects +#' +#' @param Obj Compiled TMB object +#' @return Return Tagged-list of fixed and random effects (returned invisibly) + +#' @export +list_parameters = function( Obj, verbose=TRUE ){ + Return = list() + Table = data.frame() + if( length(Obj$env$random)>0 ){ + Return[["Fixed_effects"]] = names(Obj$env$last.par[-Obj$env$random]) + Return[["Random_effects"]] = names(Obj$env$last.par[Obj$env$random]) + Table = data.frame("Coefficient_name"=names(table(Return[["Fixed_effects"]])), "Number_of_coefficients"=as.numeric(table(Return[["Fixed_effects"]])), "Type"="Fixed") + Table = rbind( Table, data.frame("Coefficient_name"=names(table(Return[["Random_effects"]])), "Number_of_coefficients"=as.numeric(table(Return[["Random_effects"]])), "Type"="Random")) + }else{ + Return[["Fixed_effects"]] = names(Obj$env$last.par) + Table = data.frame("Coefficient_name"=names(table(Return[["Fixed_effects"]])), "Number_of_coefficients"=as.numeric(table(Return[["Fixed_effects"]])), "Type"="Fixed") + } + if( verbose==TRUE ){ + message("List of estimated fixed and random effects:") + print(Table) + } + return( invisible(Table) ) +} diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 0000000..d71acfb --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,4 @@ +url: ~ +template: + bootstrap: 5 + diff --git a/man/FishBase_and_Morphometrics.Rd b/man/FishBase_and_Morphometrics.Rd index b67164a..e52768a 100644 --- a/man/FishBase_and_Morphometrics.Rd +++ b/man/FishBase_and_Morphometrics.Rd @@ -8,9 +8,10 @@ \format{ A tagged list containing data and predictions \describe{ - \item{N_factors}{Number of factors used for evolution in life-history model} + \item{text}{text file specifying the SEM structure} \item{N_obsfactors}{Number of factors used for measurent-error in life-history model} \item{beta_gv}{Predictive mean (in transformed space) among traits for every taxon in tree} + \item{Cov_gvv}{Covariance among traits for every taxon in tree} \item{Use_REML}{Boolean, whether REML was used for model} \item{ParentChild_gz}{Record of taxonomic tree} \item{ParHat}{Parameter estimates and predictions} @@ -19,6 +20,9 @@ A tagged list containing data and predictions \item{Z_ik}{Taxonomy for each datum} \item{SR_obs}{Stock-recruit records from RAM Legacy stock-recruit database} \item{StockData}{Auxiliary information for every stock with stock-recruit information} + \item{RAM}{Matrix specifying SEM parameters} + \item{SEM_model}{Description of SEM path diagram} + \item{tree}{Evolutionary tree in phylo format, for use in plotting, with order matching ParentChild_gz} ... } } diff --git a/man/Update_prediction.Rd b/man/Update_prediction.Rd deleted file mode 100644 index ea64d1f..0000000 --- a/man/Update_prediction.Rd +++ /dev/null @@ -1,52 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/update_prediction.R -\name{update_prediction} -\alias{update_prediction} -\title{Update predictions from a model} -\usage{ -update_prediction(predmean_j, predcov_jj, obscov_jj, Ynew_ij) -} -\arguments{ -\item{predmean_j}{vector of predictions for a given taxon} - -\item{predcov_jj}{matrix of estimated covariance for predictions} - -\item{obscov_jj}{matrix of estimated sampling imprecision for new samples} - -\item{Ynew_ij}{matrix of new samples (NAs are ignored and have essentially no effect)} -} -\description{ -Updates the predictive distribution for each trait analytically as - precision-weighted average of the existing prediction and new sampled values. Presumably - the function will be run such that samples are weighted by the estimated measurement-covariance. - The measurement-covariance is edited in each sample to account for missing values, - which are inputted as NA values; this approximates the analytic calculation - without requiring integrating across missing samples. -} -\examples{ -\dontrun{ - # New values - linf = c(61.1, 50.2, 41.8, 35.4, 52.8, 46.3, 53.4, 54.1, 43.9, 45.5, 50.7, 55.1) - vbk = c(0.28, 0.45, 0.57, 0.56, 0.1426, 0.3669, 0.182, 0.3764, 0.34, 0.31, 0.454, 0.246) - - # Extract estimates - predict_GP = Plot_taxa( Search_species(Genus="Macquaria",Species="ambigua", add_ancestors=FALSE)$match_taxonomy, mfrow=c(3,2) ) - - # Format new - Ynew_ij = matrix(NA, nrow=length(linf), ncol=length(predict_GP[[1]]$Mean_pred) ) - colnames(Ynew_ij) = names(predict_GP[[1]]$Mean_pred) - Ynew_ij[,"Loo"] = log(linf) - Ynew_ij[,"K"] = log(vbk) - - # Update - which_cols = which( names(predict_GP[[1]]$Mean_pred) \%in\% colnames(FishLife::FishBase_and_RAM$obsCov_jj) ) - Update = update_prediction( predmean_j = predict_GP[[1]]$Mean_pred[which_cols], - predcov_jj = predict_GP[[1]]$Cov_pred[which_cols,which_cols], - obscov_jj = FishLife::FishBase_and_RAM$obsCov_jj, - Ynew_ij = Ynew_ij[,which_cols] ) - - # Check - cbind( "Orig"=Update$updatemean_j, "New"=colMeans(Ynew_ij[,which_cols],na.rm=TRUE), "Updated"=predict_GP[[1]]$Mean_pred[which_cols] ) -} - -} diff --git a/man/fit_tmb.Rd b/man/fit_tmb.Rd new file mode 100644 index 0000000..cbe1903 --- /dev/null +++ b/man/fit_tmb.Rd @@ -0,0 +1,92 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/fit_tmb.R +\name{fit_tmb} +\alias{fit_tmb} +\title{Optimize a TMB model} +\usage{ +fit_tmb( + obj, + fn = obj$fn, + gr = obj$gr, + startpar = NULL, + lower = -Inf, + upper = Inf, + getsd = TRUE, + control = list(eval.max = 10000, iter.max = 10000, trace = 1), + bias.correct = FALSE, + bias.correct.control = list(sd = FALSE, split = NULL, nsplit = NULL, vars_to_correct = + NULL), + savedir = NULL, + loopnum = 2, + newtonsteps = 0, + n = Inf, + getReportCovariance = FALSE, + getJointPrecision = FALSE, + getHessian = FALSE, + quiet = FALSE, + start_time_elapsed = as.difftime("0:0:0"), + ... +) +} +\arguments{ +\item{obj}{The compiled TMB object} + +\item{startpar}{Starting values for fixed effects (default NULL uses \code{obj$par})} + +\item{lower, upper}{ + vectors of lower and upper bounds, replicated to be as long as + \code{start}. If unspecified, all parameters are assumed to be + unconstrained. + } + +\item{getsd}{Boolean indicating whether to run standard error calculation; see \code{\link[TMB]{sdreport}} for details} + +\item{control}{A list of control parameters. For details see \code{\link[stats]{nlminb}}} + +\item{bias.correct}{Boolean indicating whether to do epsilon bias-correction; +see \code{\link[TMB]{sdreport}} and \code{\link[TMBhelper]{fit_tmb}}for details} + +\item{bias.correct.control}{tagged list of options for epsilon bias-correction, +where \code{vars_to_correct} is a character-vector of ADREPORT variables that should be bias-corrected} + +\item{savedir}{directory to save results (if \code{savedir=NULL}, then results aren't saved)} + +\item{loopnum}{number of times to re-start optimization (where \code{loopnum=3} +sometimes achieves a lower final gradient than \code{loopnum=1})} + +\item{newtonsteps}{Integer specifying the number of extra newton steps to take +after optimization (alternative to \code{loopnum}). +Each newtonstep requires calculating the Hessian matrix and is therefore slow. +But for well-behaved models, each Newton step will typically +decrease the maximum gradient of the loglikelihood with respect to each fixed effect, +and therefore this option can be used to achieve an arbitrarily low final gradient +given sufficient time for well-behaved models. However, this option will also +perform strangely or have unexpected consequences for poorly-behaved models, e.g., +when fixed effects are at upper or lower bounds.} + +\item{n}{sample sizes (if \code{n!=Inf} then \code{n} is used to calculate BIC and AICc)} + +\item{getReportCovariance}{Get full covariance matrix of ADREPORTed variables?} + +\item{getJointPrecision}{Optional. Return full joint precision matrix of random effects and parameters?} + +\item{getHessian}{return Hessian for usage in later code} + +\item{quiet}{Boolean whether to print additional messages results to terminal} + +\item{start_time_elapsed}{how much time has elapsed prior to calling fit_tmb, +for use, e.g., when calling \code{fit_tmb} multiple times in sequence, +where \code{start_time_elapsed = opt_previous$time_for_run}} + +\item{...}{list of settings to pass to \code{\link[TMB]{sdreport}}} +} +\value{ +the standard output from \code{\link[stats]{nlminb}}, except with additional diagnostics and timing info, + and a new slot containing the output from \code{\link[TMB]{sdreport}} +} +\description{ +\code{fit_tmb} runs a TMB model and generates standard diagnostics +} +\references{ +For more details see \url{https://doi.org/10.1016/j.fishres.2015.11.016} +} diff --git a/man/list_parameters.Rd b/man/list_parameters.Rd new file mode 100644 index 0000000..8361ac6 --- /dev/null +++ b/man/list_parameters.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/list_parameters.R +\name{list_parameters} +\alias{list_parameters} +\title{List fixed and random effects} +\usage{ +list_parameters(Obj, verbose = TRUE) +} +\arguments{ +\item{Obj}{Compiled TMB object} +} +\value{ +Return Tagged-list of fixed and random effects (returned invisibly) +} +\description{ +\code{list_parameters} lists all fixed and random effects +} diff --git a/scratch/build_pkgdown_website.R b/scratch/build_pkgdown_website.R new file mode 100644 index 0000000..3d80907 --- /dev/null +++ b/scratch/build_pkgdown_website.R @@ -0,0 +1,39 @@ +# Author: Kevin See +# Purpose: Build a website for this package using pkgdown +# Created: 5/17/2021 +# Last Modified: 5/17/2021 +# Notes: Based on instructions found here: https://pkgdown.r-lib.org/index.html + +setwd(R'(C:\Users\James.Thorson\Desktop\Git\FishLife)') + +#----------------------------------------------------------------- +# load needed libraries +library(pkgdown) + + +# Only needed once +if( FALSE ){ + # set up to automatically publish pkgdown site to GitHub + # usethis::create_github_token() + # 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 + # gitcreds::gitcreds_set(url = "https://github.com") + usethis::use_pkgdown_github_pages() + + + # Run once to configure your package to use pkgdown + usethis::use_pkgdown() + + # check that _pkgdown.yml looks good + pkgdown::check_pkgdown() +} + +# Run to build the website +pkgdown::build_site( examples=TRUE ) +# build_articles( lazy = FALSE ) + +# to look at the site +pkgdown::preview_site() + +#----------------------------------------------------------------- +# deploy site to gh-pages branch on GitHub +pkgdown::deploy_to_branch() diff --git a/vignettes/Archetypes.Rmd b/vignettes/Archetypes.Rmd new file mode 100644 index 0000000..a8858b2 --- /dev/null +++ b/vignettes/Archetypes.Rmd @@ -0,0 +1,113 @@ +--- +title: "Archetypes" +author: "James Thorson" +date: "`r Sys.Date()`" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Archetypes} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +# Description + +We here show how to replicate and extract the archetypes estimated in Thorson et al. 2023. We first load packages and data: + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +# Build +# setwd(R'(C:\Users\James.Thorson\Desktop\Git\FishLife)'); devtools::build_rmd("vignettes/Archetypes.Rmd"); rmarkdown::render( "vignettes/Archetypes.Rmd", rmarkdown::pdf_document()) +``` + +```{r, echo=TRUE, message=FALSE} +# Load packages and data +library(archetypes) +library(FishLife) +data(FishBase_and_Morphometrics) + +# Set seed so that archetypes are labeled the same way when replicating +suppressWarnings(RNGversion("3.5.0")) +set.seed(1986) +``` + +Next we re-run the archetype analysis and plot the resulting simplex of archetypes + +```{r, echo=TRUE, message=FALSE, fig.width=6, fig.height=6} +# Load results +beta_iv = FishBase_and_Morphometrics$beta_gv[ FishBase_and_Morphometrics$g_i, ] +a <- archetypes(beta_iv, 3, verbose = TRUE) + +# Labels +Labels = c("Equil.","Oppor.","Per.")[c(1,3,2)] +Col_names = colnames(FishBase_and_Morphometrics$Y_ij) + Col_names = gsub("habitat", "H: ", Col_names ) + Col_names = gsub("body_shape", "BS: ", Col_names ) + Col_names = gsub("feeding_mode", "FM: ", Col_names ) + Col_names = gsub("spawning_type", "ST: ", Col_names ) + +# Col function +mycol = function(x,alpha){ + y = col2rgb(x) + rgb( y[1,1]/255, y[2,1]/255, y[3,1]/255, alpha=alpha) +} + +# Plot by taxa +Z_ik = FishBase_and_Morphometrics$Z_ik +simplex = simplexplot(a, show_points=FALSE, labels=Labels) #, projection=atypes_projection) +points( simplex$proj_h[which(Z_ik$Family=="Gobiidae"), ], col=mycol("brown",0.5), pch=20 ) +points( simplex$proj_h[which(Z_ik$Family=="Percidae"), ], col=mycol("orange",0.5), pch=20 ) +points( simplex$proj_h[which(Z_ik$Family=="Salmonidae"), ], col=mycol("red",0.5), pch=20 ) +points( simplex$proj_h[which(Z_ik$Family=="Scorpaenidae"), ], col=mycol("grey",0.5), pch=20 ) +points( simplex$proj_h[which(Z_ik$Family=="Clupeidae"), ], col=mycol("green",0.5), pch=20 ) +points( simplex$proj_h[which(Z_ik$Class=="Elasmobranchii"), ], col=mycol("black",0.5), pch=20 ) +points( simplex$proj_h[which(Z_ik$Family=="Poeciliidae"), ], col=mycol("blue",0.5), pch=20 ) +points( simplex$proj_h[which(Z_ik$Family=="Atherinidae"), ], col=mycol("purple",0.5), pch=20 ) +legend( "topleft", bty="n", ncol=2, legend=c("Gobiidae","Salmonidae","Poeciliidae", + "Atherinidae","Percidae","Scorpaenidae","Clupeidae","Elasmobranchii"), + fill=c("brown","red","blue","purple","orange","grey","green","black")) +``` + +If interested, we can pull out values for individual species based on their Latin binomial. We here show a few examples: + +```{r, echo=TRUE, message=FALSE, fig.width=6, fig.height=6} +# Pull out examples +Z_ik = FishBase_and_Morphometrics$Z_ik +species_to_plot = data.frame( + c("Gadus_macrocephalus", "Hippoglossus_stenolepis", "Sebastes_alutus", "Thaleichthys_pacificus", + "Oncorhynchus_tshawytscha", "Somniosus_pacificus", "Mallotus_villosus", "Boreogadus_saida", + "Bothrocara_brunneum", "Tarletonbeania_taylori", "Gambusia_affinis", "Callogobius_sclateri", + "Acanthurus_xanthopterus", "Eviota_jewettae"), + c( "Pacific cod", "Halibut", "Paciric Ocean perch", "Eulachon", "Chinook", "Sleeper shark", + "Capelin", "Arctic cod", "twoline eelpout", "North Pacific lanternfish", + "Western mosquitofish", "Pacific goby", "Yellowfin surgeonfish", "Jewett's dwarfgoby") +) +species_to_plot$color = rainbow(nrow(species_to_plot)) +# Plot by taxa +simplex = simplexplot(a, show_points=FALSE, labels=Labels) +for( r in seq_len(nrow(species_to_plot)) ){ + which_row = which(paste0(Z_ik$Genus,"_",Z_ik$Species)==species_to_plot[r,1]) + text( x=simplex$proj_h[which_row,1], y=simplex$proj_h[which_row,2], + col=species_to_plot[r,3], labels=letters[r] ) +} +legend( "topleft", bty="n", ncol=2, legend=species_to_plot[,2], fill=species_to_plot[,3]) +``` + +Finally, we can plot the distribution of predicted traits for all species, along with the predicted mean for each archetype: + +```{r, echo=TRUE, message=FALSE} +Col_names = colnames(FishBase_and_Morphometrics$beta_gv) + Col_names = gsub("habitat", "H: ", Col_names ) + Col_names = gsub("body_shape", "BS: ", Col_names ) + Col_names = gsub("feeding_mode", "FM: ", Col_names ) + Col_names = gsub("spawning_type", "ST: ", Col_names ) +dims = c(6,6) +par( mfrow=dims, mgp=c(2,0.5,0), mar=c(1,1,2,0), tck=-0.02 ) +for( cI in 1:ncol(FishBase_and_Morphometrics$beta_gv) ){ + hist( FishBase_and_Morphometrics$beta_gv[,cI], + main=Col_names[cI], xlab="", ylab="", yaxt="n" ) + abline( v=parameters(a)[,cI], col=viridisLite::viridis(3), lwd=3 ) +} +``` \ No newline at end of file diff --git a/vignettes/Archetypes.html b/vignettes/Archetypes.html new file mode 100644 index 0000000..fc9ceec --- /dev/null +++ b/vignettes/Archetypes.html @@ -0,0 +1,456 @@ + + + + + + + + + + + + + + + + +Archetypes + + + + + + + + + + + + + + + + + + + + + + + + + + +

Archetypes

+

James Thorson

+

2023-12-17

+ + + +
+

Description

+

We here show how to replicate and extract the archetypes estimated in +Thorson et al. 2023. We first load packages and data:

+
# Load packages and data
+library(archetypes)
+library(FishLife)
+data(FishBase_and_Morphometrics)
+
+# Set seed so that archetypes are labeled the same way when replicating
+suppressWarnings(RNGversion("3.5.0"))
+set.seed(1986)
+

Next we re-run the archetype analysis and plot the resulting simplex +of archetypes

+
# Load results
+beta_iv = FishBase_and_Morphometrics$beta_gv[ FishBase_and_Morphometrics$g_i, ]
+a <- archetypes(beta_iv, 3, verbose = TRUE)
+#> 1: rss = 0.00844337, improvement = 0.00939731
+#> 2: rss = 0.00789766, improvement = 0.00054571
+#> 3: rss = 0.00779479, improvement = 0.00010287
+#> 4: rss = 0.00778014, improvement = 0.00001465
+#> 5: rss = 0.00777922, improvement = 0.00000092
+#> 6: rss = 0.00777984, improvement = -0.00000062
+
+# Labels
+Labels = c("Equil.","Oppor.","Per.")[c(1,3,2)]
+Col_names = colnames(FishBase_and_Morphometrics$Y_ij)
+  Col_names = gsub("habitat", "H: ", Col_names )
+  Col_names = gsub("body_shape", "BS: ", Col_names )
+  Col_names = gsub("feeding_mode", "FM: ", Col_names )
+  Col_names = gsub("spawning_type", "ST: ", Col_names )
+
+# Col function
+mycol = function(x,alpha){
+  y = col2rgb(x)
+  rgb( y[1,1]/255, y[2,1]/255, y[3,1]/255, alpha=alpha)
+}
+
+# Plot by taxa
+Z_ik = FishBase_and_Morphometrics$Z_ik
+simplex = simplexplot(a, show_points=FALSE, labels=Labels) #, projection=atypes_projection)
+points( simplex$proj_h[which(Z_ik$Family=="Gobiidae"), ], col=mycol("brown",0.5), pch=20 )
+points( simplex$proj_h[which(Z_ik$Family=="Percidae"), ], col=mycol("orange",0.5), pch=20 )
+points( simplex$proj_h[which(Z_ik$Family=="Salmonidae"), ], col=mycol("red",0.5), pch=20 )
+points( simplex$proj_h[which(Z_ik$Family=="Scorpaenidae"), ], col=mycol("grey",0.5), pch=20 )
+points( simplex$proj_h[which(Z_ik$Family=="Clupeidae"), ], col=mycol("green",0.5), pch=20 )
+points( simplex$proj_h[which(Z_ik$Class=="Elasmobranchii"), ], col=mycol("black",0.5), pch=20 )
+points( simplex$proj_h[which(Z_ik$Family=="Poeciliidae"), ], col=mycol("blue",0.5), pch=20 )
+points( simplex$proj_h[which(Z_ik$Family=="Atherinidae"), ], col=mycol("purple",0.5), pch=20 )
+legend( "topleft", bty="n", ncol=2, legend=c("Gobiidae","Salmonidae","Poeciliidae",
+        "Atherinidae","Percidae","Scorpaenidae","Clupeidae","Elasmobranchii"),
+        fill=c("brown","red","blue","purple","orange","grey","green","black"))
+

+

If interested, we can pull out values for individual species based on +their Latin binomial. We here show a few examples:

+
# Pull out examples
+Z_ik = FishBase_and_Morphometrics$Z_ik
+species_to_plot = data.frame(
+  c("Gadus_macrocephalus", "Hippoglossus_stenolepis", "Sebastes_alutus", "Thaleichthys_pacificus",
+     "Oncorhynchus_tshawytscha", "Somniosus_pacificus", "Mallotus_villosus", "Boreogadus_saida",
+     "Bothrocara_brunneum", "Tarletonbeania_taylori", "Gambusia_affinis", "Callogobius_sclateri",
+     "Acanthurus_xanthopterus", "Eviota_jewettae"),
+  c( "Pacific cod", "Halibut", "Paciric Ocean perch", "Eulachon", "Chinook", "Sleeper shark",
+     "Capelin", "Arctic cod", "twoline eelpout", "North Pacific lanternfish",
+     "Western mosquitofish", "Pacific goby", "Yellowfin surgeonfish", "Jewett's dwarfgoby")
+)
+species_to_plot$color = rainbow(nrow(species_to_plot))
+# Plot by taxa
+simplex = simplexplot(a, show_points=FALSE, labels=Labels)
+for( r in seq_len(nrow(species_to_plot)) ){
+  which_row = which(paste0(Z_ik$Genus,"_",Z_ik$Species)==species_to_plot[r,1])
+  text( x=simplex$proj_h[which_row,1], y=simplex$proj_h[which_row,2],
+        col=species_to_plot[r,3], labels=letters[r] )
+}
+legend( "topleft", bty="n", ncol=2, legend=species_to_plot[,2], fill=species_to_plot[,3])
+

+

Finally, we can plot the distribution of predicted traits for all +species, along with the predicted mean for each archetype:

+
Col_names = colnames(FishBase_and_Morphometrics$beta_gv)
+  Col_names = gsub("habitat", "H: ", Col_names )
+  Col_names = gsub("body_shape", "BS: ", Col_names )
+  Col_names = gsub("feeding_mode", "FM: ", Col_names )
+  Col_names = gsub("spawning_type", "ST: ", Col_names )
+dims = c(6,6)
+par( mfrow=dims, mgp=c(2,0.5,0), mar=c(1,1,2,0), tck=-0.02 )
+for( cI in 1:ncol(FishBase_and_Morphometrics$beta_gv) ){
+  hist( FishBase_and_Morphometrics$beta_gv[,cI],
+        main=Col_names[cI], xlab="", ylab="", yaxt="n" )
+  abline( v=parameters(a)[,cI], col=viridisLite::viridis(3), lwd=3 )
+}
+

+
+ + + + + + + + + + + diff --git a/vignettes/Archetypes.pdf b/vignettes/Archetypes.pdf new file mode 100644 index 0000000000000000000000000000000000000000..549f43c01dd1f5e4297f1428f2f4cafc1d51bfd7 GIT binary patch literal 418856 zcma%jbyQSe^sn(1B$e(|x&)+4>F(}EI)@IC?go(o6zLwiL2AfBxLgpcVLvC+uh0M>7%pz-MXW{aJoR#A( z8~N{F$SmTPHZEpP#iB+e8xQga3+sp0f1V#mOX=N!7>||v# z*0(ZrxdT*VR4q#IXXs0zjz)?EYp3cAY4vO)psx%bCql?Z=mi$#pZc)qNzC3jX2<*JlmD< zn$XJCXtA5ge9Q-8Hx`C~g_>H0hrfV2lWweN947Je ze(?&__^S9ENxD!mL#%$+f7G)qfsuqpMa%TiFsX2!RRsxulF(_XGx+6nkgwaxShKK?%8*wm^Y^Xg!DFJ-HO_PUian*yHwGqY?-}2nD%oXsyILaH zbX;Kt^9L$OBSaE9DL%rIf0=_LZszZzYcq`&`V3q$jW$s}u3*?v_145yr2Sp-Y##-V zVOXWArydHn63Inv<=CR)OSByw zEMcmY6Elp{<+K@s$@;P240U5I1v~j|b)T>DJXq@vA5L>puS1gld)cvc^wX!7WA>e| z_5^-&$?%}rM#N*2=Mh(JxF{0M7h{H3`PX78&m6SF?9(X+3O^XQdGC{I8|T0KFN2D4 zAc?n5!?$+Wr9r}c>_Ur!BV$5-U;Sy~ci;F~tDCBJaa+5$=ZH9F}~3BI`~fTQ2J^(XndVwHlH*(f%K+Segl@nXEBl zt|6WR{7K>m1Zg3p#G)LlwR5_-q9<|;rU8)yK7qGpOZa2VK3?b37MZ0hNW9aDpLXaj 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