first gp() additions

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: mvgam
 Title: Multivariate (Dynamic) Generalized Additive Models
-Version: 1.0.5
-Date: 2023-06-03
+Version: 1.0.6
+Date: 2023-10-06
 Authors@R:
     person("Nicholas J", "Clark", , "nicholas.j.clark1214@gmail.com", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0001-7131-3301"))

NAMESPACE

@@ -78,6 +78,7 @@ export(plot_mvgam_series)
 export(plot_mvgam_smooth)
 export(plot_mvgam_trend)
 export(plot_mvgam_uncertainty)
+export(plot_predictions.mvgam)
 export(ppc)
 export(roll_eval_mvgam)
 export(score)
@@ -131,6 +132,7 @@ importFrom(magrittr,"%>%")
 importFrom(marginaleffects,get_coef)
 importFrom(marginaleffects,get_predict)
 importFrom(marginaleffects,get_vcov)
+importFrom(marginaleffects,plot_predictions)
 importFrom(marginaleffects,set_coef)
 importFrom(methods,new)
 importFrom(mgcv,bam)
@@ -150,6 +152,7 @@ importFrom(posterior,as_draws_rvars)
 importFrom(posterior,rhat)
 importFrom(posterior,variables)
 importFrom(rlang,missing_arg)
+importFrom(rlang,parse_expr)
 importFrom(rstantools,posterior_epred)
 importFrom(rstantools,posterior_linpred)
 importFrom(rstantools,posterior_predict)
@@ -169,7 +172,9 @@ importFrom(stats,dlnorm)
 importFrom(stats,dnbinom)
 importFrom(stats,dnorm)
 importFrom(stats,dpois)
+importFrom(stats,drop.terms)
 importFrom(stats,ecdf)
+importFrom(stats,fitted)
 importFrom(stats,formula)
 importFrom(stats,frequency)
 importFrom(stats,gaussian)

R/compute_edf.R

@@ -1,4 +1,5 @@
 #' Compute approximate EDFs of smooths
+#' @importFrom stats fitted
 #'@noRd
 compute_edf = function(mgcv_model, object, rho_names, sigma_raw_names){
 
@@ -91,7 +92,11 @@ compute_edf = function(mgcv_model, object, rho_names, sigma_raw_names){
         (mgcv_model$off[i] + ncol(mgcv_model$S[[i]]) - 1)
       XWXS[ind, ind] <- XWXS[ind, ind] + mgcv_model$S[[i]] * lambda[i]
     }
-    edf <- diag(solve(XWXS, XWX))
+    suppressWarnings(edf <- try(diag(solve(XWXS, XWX)), silent = TRUE))
+    if(inherits(edf, 'try-error')){
+      edf <- rep(1, length(coef(mgcv_model)))
+      names(edf) <- names(coef(mgcv_model))
+    }
     mgcv_model$edf <- edf
   }
 

R/dynamic.R

@@ -145,6 +145,24 @@ interpret_mvgam = function(formula, N){
 
   attr(newformula, '.Environment') <- attr(formula, '.Environment')
 
+  # Check if any terms use the gp wrapper, as mvgam cannot handle
+  # multivariate GPs yet
+  response <- terms.formula(newformula)[[2]]
+  tf <- terms.formula(newformula, specials = c("gp"))
+  which_gp <- attr(tf,"specials")$gp
+  if(length(which_gp) != 0L){
+    gp_details <- vector(length = length(which_gp),
+                          mode = 'list')
+      for(i in seq_along(which_gp)){
+        gp_details[[i]] <- eval(parse(text = rownames(attr(tf,
+                                                            "factors"))[which_gp[i]]))
+      }
+    if(any(unlist(lapply(purrr::map(gp_details, 'term'), length)) > 1)){
+      stop('mvgam cannot yet handle multidimensional gps',
+           call. = FALSE)
+    }
+  }
+
   # Check if any terms use the dynamic wrapper
   response <- terms.formula(newformula)[[2]]
   tf <- terms.formula(newformula, specials = c("dynamic"))

R/get_linear_predictors.R

@@ -26,6 +26,47 @@ obs_Xp_matrix = function(newdata, mgcv_model){
                                     type = 'lpmatrix'))
   }
 
+  # Check for any gp() terms and update the design matrix
+  # accordingly
+  if(!is.null(attr(mgcv_model, 'gp_att_table'))){
+    # Compute the eigenfunctions from the supplied attribute table,
+    # and add them to the Xp matrix
+
+    # Extract GP attributes
+    gp_att_table <- attr(mgcv_model, 'gp_att_table')
+    gp_covariates <- unlist(purrr::map(gp_att_table, 'covariate'))
+    by <- unlist(purrr::map(gp_att_table, 'by'))
+    level <- unlist(purrr::map(gp_att_table, 'level'))
+    k <- unlist(purrr::map(gp_att_table, 'k'))
+    scale <- unlist(purrr::map(gp_att_table, 'scale'))
+    mean <- unlist(purrr::map(gp_att_table, 'mean'))
+    max_dist <- unlist(purrr::map(gp_att_table, 'max_dist'))
+    boundary <- unlist(purrr::map(gp_att_table, 'boundary'))
+    L <- unlist(purrr::map(gp_att_table, 'L'))
+
+    # Compute eigenfunctions
+    test_eigenfunctions <- lapply(seq_along(gp_covariates), function(x){
+      prep_eigenfunctions(data = newdata,
+                          covariate = gp_covariates[x],
+                          by = by[x],
+                          level = level[x],
+                          k = k[x],
+                          boundary = boundary[x],
+                          L = L[x],
+                          mean = mean[x],
+                          scale = scale[x],
+                          max_dist = max_dist[x])
+    })
+
+    # Find indices to replace in the design matrix and replace with
+    # the computed eigenfunctions
+    starts <- purrr::map(gp_att_table, 'first_coef')
+    ends <- purrr::map(gp_att_table, 'last_coef')
+    for(i in seq_along(starts)){
+      Xp[,c(starts[[i]]:ends[[i]])] <- test_eigenfunctions[[i]]
+    }
+  }
+
   return(Xp)
 }
 
@@ -46,31 +87,6 @@ trend_Xp_matrix = function(newdata, trend_map, series = 'all',
   trend_test$series <- trend_indicators
   trend_test$y <- NULL
 
-  # Because these are set up inherently as dynamic factor models,
-  # we ALWAYS need to forecast the full set of trends, regardless of
-  # which series (or set of series) is being forecast
-  # data.frame(series = trend_test$series,
-  #            time = trend_test$time,
-  #            row_num = 1:length(trend_test$time)) %>%
-  #   dplyr::group_by(series, time) %>%
-  #   dplyr::slice_head(n = 1) %>%
-  #   dplyr::ungroup() %>%
-  #   dplyr::arrange(time, series) %>%
-  #   dplyr::pull(row_num) -> inds_keep
-  #
-  # if(inherits(newdata, 'list')){
-  #   trend_test <- lapply(trend_test, function(x){
-  #     if(is.matrix(x)){
-  #       matrix(x[inds_keep,], ncol = NCOL(x))
-  #     } else {
-  #       x[inds_keep]
-  #     }
-  #
-  #   })
-  # } else {
-  #   trend_test <- trend_test[inds_keep, ]
-  # }
-
   suppressWarnings(Xp_trend  <- try(predict(mgcv_model,
                                             newdata = trend_test,
                                             type = 'lpmatrix'),
@@ -95,6 +111,48 @@ trend_Xp_matrix = function(newdata, trend_map, series = 'all',
                                           newdata = testdat,
                                           type = 'lpmatrix'))
   }
+
+  # Check for any gp() terms and update the design matrix
+  # accordingly
+  if(!is.null(attr(mgcv_model, 'gp_att_table'))){
+    # Compute the eigenfunctions from the supplied attribute table,
+    # and add them to the Xp matrix
+
+    # Extract GP attributes
+    gp_att_table <- attr(mgcv_model, 'gp_att_table')
+    gp_covariates <- unlist(purrr::map(gp_att_table, 'covariate'))
+    by <- unlist(purrr::map(gp_att_table, 'by'))
+    level <- unlist(purrr::map(gp_att_table, 'level'))
+    k <- unlist(purrr::map(gp_att_table, 'k'))
+    scale <- unlist(purrr::map(gp_att_table, 'scale'))
+    mean <- unlist(purrr::map(gp_att_table, 'mean'))
+    max_dist <- unlist(purrr::map(gp_att_table, 'max_dist'))
+    boundary <- unlist(purrr::map(gp_att_table, 'boundary'))
+    L <- unlist(purrr::map(gp_att_table, 'L'))
+
+    # Compute eigenfunctions
+    test_eigenfunctions <- lapply(seq_along(gp_covariates), function(x){
+      prep_eigenfunctions(data = newdata,
+                          covariate = gp_covariates[x],
+                          by = by[x],
+                          level = level[x],
+                          k = k[x],
+                          boundary = boundary[x],
+                          L = L[x],
+                          mean = mean[x],
+                          scale = scale[x],
+                          max_dist = max_dist[x])
+    })
+
+    # Find indices to replace in the design matrix and replace with
+    # the computed eigenfunctions
+    starts <- purrr::map(gp_att_table, 'first_coef')
+    ends <- purrr::map(gp_att_table, 'last_coef')
+    for(i in seq_along(starts)){
+      Xp_trend[,c(starts[[i]]:ends[[i]])] <- test_eigenfunctions[[i]]
+    }
+  }
+
   return(Xp_trend)
 }
 

R/get_mvgam_priors.R

@@ -223,6 +223,31 @@ get_mvgam_priors = function(formula,
   # Ensure series and time variables are present
   data_train <- validate_series_time(data_train, name = 'data')
 
+  # Check for gp and mo terms in the formula
+  orig_formula <- gp_terms <- mo_terms <- NULL
+  if(any(grepl('gp(', attr(terms(formula), 'term.labels'), fixed = TRUE))){
+
+    # Check that there are no multidimensional gp terms
+    formula <- interpret_mvgam(formula, N = max(data_train$time))
+    orig_formula <- formula
+
+    # Keep intercept?
+    keep_intercept <- attr(terms(formula), 'intercept') == 1
+
+    # Indices of gp() terms in formula
+    gp_terms <- which_are_gp(formula)
+
+    # Extract GP attributes
+    gp_details <- get_gp_attributes(formula)
+
+    # Replace with s() terms so the correct terms are included
+    # in the model.frame
+    formula <- gp_to_s(formula)
+    if(!keep_intercept){
+      formula <- update(formula, trend_y  ~ . -1)
+    }
+  }
+
   # Check for missing rhs in formula
   drop_obs_intercept <- FALSE
   if(length(attr(terms(formula), 'term.labels')) == 0 &
@@ -240,7 +265,9 @@ get_mvgam_priors = function(formula,
            call. = FALSE)
     }
   }
-  orig_formula <- formula
+  if(is.null(orig_formula)){
+    orig_formula <- formula
+  }
 
   # Validate observation formula
   formula <- interpret_mvgam(formula, N = max(data_train$time))

R/globals.R

@@ -8,4 +8,5 @@ utils::globalVariables(c("y", "year", "smooth_vals", "smooth_num",
                          "mod_call", "particles", "obs", "mgcv_model",
                          "param_name", "outcome", "mgcv_plottable",
                          "term", "data_test", "object", "row_num", "trends_test",
-                         "trend", "trend_series", "trend_y", ".", "gam"))
+                         "trend", "trend_series", "trend_y", ".", "gam",
+                         "group", "mod", "row_id"))