add gratia to Enhances and conditionally load methods for monotonic e…

…valuation

DESCRIPTION

@@ -53,9 +53,10 @@ Suggests:
     coda,
     runjags,
     tibble (>= 3.0.0),
-    gratia,
     usethis,
     testthat
+Enhances:
+    gratia
 Additional_repositories: https://mc-stan.org/r-packages/
 LinkingTo: Rcpp, RcppArmadillo
 VignetteBuilder: knitr

NAMESPACE

@@ -14,8 +14,6 @@ S3method(as_draws_matrix,mvgam)
 S3method(as_draws_rvars,mvgam)
 S3method(coef,mvgam)
 S3method(conditional_effects,mvgam)
-S3method(eval_smooth,mod.smooth)
-S3method(eval_smooth,moi.smooth)
 S3method(find_predictors,mvgam)
 S3method(find_predictors,mvgam_prefit)
 S3method(fitted,mvgam)
@@ -78,7 +76,8 @@ export(code)
 export(compare_mvgams)
 export(dynamic)
 export(eval_mvgam)
-export(eval_smooth)
+export(eval_smoothDotmodDotsmooth)
+export(eval_smoothDotmoiDotsmooth)
 export(forecast)
 export(get_mvgam_priors)
 export(hindcast)

R/monotonic.R

@@ -413,20 +413,54 @@ add_mono_model_file = function(model_file,
               model_data = model_data))
 }
 
-#' S3 methods to evaluate individual smooths
-#' @param smooth currently an object that inherits from class `mgcv.smooth`
-#' @param ... arguments passed to other methods
-#' @export
-`eval_smooth` <- function(smooth, ...) {
-  UseMethod("eval_smooth")
+# Add moi and mod smooth eval_smooth methods to gratia namespace
+# on load
+.onLoad <- function(libname, pkgname) {
+  if(requireNamespace("gratia", quietly = TRUE)){
+    registerS3method("eval_smooth",
+                     "moi.smooth",
+                     eval_smoothDotmoiDotsmooth,
+                     envir = asNamespace("gratia"))
+    registerS3method("eval_smooth",
+                     "mod.smooth",
+                     eval_smoothDotmodDotsmooth,
+                     envir = asNamespace("gratia"))
+  }
 }
 
-#' Evaluation of a monotonically increasing function
+#' Evaluation of a monotonic functions in mvgam
 #' These evaluation functions are needed so that gratia::draw methods work with mvgam
 #' monotonic smooths
-#' @rdname monotonic
+#' @rdname eval_smooth_monotonic
+#' @param model an object of class `"gam"`
+#' @param smooth a smooth object of class `"moi.smooth"` or `"mod.smooth"`
+#' @param n numeric; the number of points over the range of the covariate at
+#'   which to evaluate the smooth.
+#' @param n_3d,n_4d numeric; the number of points over the range of last
+#'   covariate in a 3D or 4D smooth. The default is `NULL` which achieves the
+#'   standard behaviour of using `n` points over the range of all covariate,
+#'   resulting in `n^d` evaluation points, where `d` is the dimension of the
+#'   smooth. For `d > 2` this can result in very many evaluation points and slow
+#'   performance. For smooths of `d > 4`, the value of `n_4d` will be used for
+#'   all dimensions `> 4`, unless this is `NULL`, in which case the default
+#'   behaviour (using `n` for all dimensions) will be observed.
+#' @param data a data frame of covariate values at which to evaluate the
+#'   smooth.
+#' @param unconditional logical; should confidence intervals include the
+#'   uncertainty due to smoothness selection? If `TRUE`, the corrected Bayesian
+#'   covariance matrix will be used.
+#' @param overall_uncertainty logical; should the uncertainty in the model
+#'  constant term be included in the standard error of the evaluate values of
+#'  the smooth?
+#' @param dist numeric; if greater than 0, this is used to determine when
+#'   a location is too far from data to be plotted when plotting 2-D smooths.
+#'   The data are scaled into the unit square before deciding what to exclude,
+#'   and `dist` is a distance within the unit square. See
+#'   [mgcv::exclude.too.far()] for further details.
+#' @param ... ignored.
+#' @aliases eval_smooth.mod.smooth
 #' @export
-eval_smooth.moi.smooth = function(smooth,
+eval_smoothDotmodDotsmooth = function(smooth,
                                   model,
                                   n = 100,
                                   n_3d = NULL,
@@ -436,7 +470,6 @@ eval_smooth.moi.smooth = function(smooth,
                                   overall_uncertainty = TRUE,
                                   dist = NULL,
                                   ...) {
-
   insight::check_if_installed("gratia")
   model$cmX <- model$coefficients
 
@@ -446,7 +479,7 @@ eval_smooth.moi.smooth = function(smooth,
     n = n, n_3d = n_3d, n_4d = n_4d,
     id = which_smooth(
       model,
-      smooth_label(smooth)
+      gratia::smooth_label(smooth)
     )
   )
 
@@ -466,7 +499,7 @@ eval_smooth.moi.smooth = function(smooth,
   ## add on info regarding by variable
   eval_sm <- add_by_var_column(eval_sm, by_var = by_var)
   ## add on spline type info
-  eval_sm <- add_smooth_type_column(eval_sm, sm_type = "Mono inc P spline")
+  eval_sm <- add_smooth_type_column(eval_sm, sm_type = "Mono dec P spline")
 
   # set some values to NA if too far from the data
   if (gratia::smooth_dim(smooth) == 2L && (!is.null(dist) && dist > 0)) {
@@ -481,45 +514,20 @@ eval_smooth.moi.smooth = function(smooth,
   eval_sm
 }
 
-#' Evaluation of a monotonically decreasing function
-#' @rdname monotonic
-#' @param model an object of class `"gam"`
-#' @param smooth a smooth object of class `"moi.smooth"` or `"mod.smooth"`
-#' @param n numeric; the number of points over the range of the covariate at
-#'   which to evaluate the smooth.
-#' @param n_3d,n_4d numeric; the number of points over the range of last
-#'   covariate in a 3D or 4D smooth. The default is `NULL` which achieves the
-#'   standard behaviour of using `n` points over the range of all covariate,
-#'   resulting in `n^d` evaluation points, where `d` is the dimension of the
-#'   smooth. For `d > 2` this can result in very many evaluation points and slow
-#'   performance. For smooths of `d > 4`, the value of `n_4d` will be used for
-#'   all dimensions `> 4`, unless this is `NULL`, in which case the default
-#'   behaviour (using `n` for all dimensions) will be observed.
-#' @param data a data frame of covariate values at which to evaluate the
-#'   smooth.
-#' @param unconditional logical; should confidence intervals include the
-#'   uncertainty due to smoothness selection? If `TRUE`, the corrected Bayesian
-#'   covariance matrix will be used.
-#' @param overall_uncertainty logical; should the uncertainty in the model
-#'  constant term be included in the standard error of the evaluate values of
-#'  the smooth?
-#' @param dist numeric; if greater than 0, this is used to determine when
-#'   a location is too far from data to be plotted when plotting 2-D smooths.
-#'   The data are scaled into the unit square before deciding what to exclude,
-#'   and `dist` is a distance within the unit square. See
-#'   [mgcv::exclude.too.far()] for further details.
-#' @param ... ignored.
+#' @rdname eval_smooth_monotonic
+#' @aliases eval_smooth.moi.smooth
 #' @export
-eval_smooth.mod.smooth = function(smooth,
-                                  model,
-                                  n = 100,
-                                  n_3d = NULL,
-                                  n_4d = NULL,
-                                  data = NULL,
-                                  unconditional = FALSE,
-                                  overall_uncertainty = TRUE,
-                                  dist = NULL,
-                                  ...) {
+eval_smoothDotmoiDotsmooth = function(smooth,
+                                      model,
+                                      n = 100,
+                                      n_3d = NULL,
+                                      n_4d = NULL,
+                                      data = NULL,
+                                      unconditional = FALSE,
+                                      overall_uncertainty = TRUE,
+                                      dist = NULL,
+                                      ...) {
+
   insight::check_if_installed("gratia")
   model$cmX <- model$coefficients
 
@@ -529,7 +537,7 @@ eval_smooth.mod.smooth = function(smooth,
     n = n, n_3d = n_3d, n_4d = n_4d,
     id = which_smooth(
       model,
-      smooth_label(smooth)
+      gratia::smooth_label(smooth)
     )
   )
 
@@ -549,7 +557,7 @@ eval_smooth.mod.smooth = function(smooth,
   ## add on info regarding by variable
   eval_sm <- add_by_var_column(eval_sm, by_var = by_var)
   ## add on spline type info
-  eval_sm <- add_smooth_type_column(eval_sm, sm_type = "Mono dec P spline")
+  eval_sm <- add_smooth_type_column(eval_sm, sm_type = "Mono inc P spline")
 
   # set some values to NA if too far from the data
   if (gratia::smooth_dim(smooth) == 2L && (!is.null(dist) && dist > 0)) {
@@ -591,7 +599,7 @@ eval_smooth.mod.smooth = function(smooth,
   if (is.gamm(object) || is.gamm4(object)) {
     object <- object[["gam"]]
   }
-  smooths <- smooths(object)
+  smooths <- gratia::smooths(object)
   which(term == smooths)
 }
 
@@ -600,7 +608,7 @@ eval_smooth.mod.smooth = function(smooth,
     data, model, n, n_3d, n_4d, id,
     var_order = NULL) {
   if (is.null(data)) {
-    data <- smooth_data(
+    data <- gratia::smooth_data(
       model = model,
       n = n,
       n_3d = n_3d,
@@ -609,9 +617,9 @@ eval_smooth.mod.smooth = function(smooth,
       var_order = var_order
     )
   } else {
-    smooth <- get_smooths_by_id(model, id)[[1L]]
-    vars <- smooth_variable(smooth)
-    by_var <- by_variable(smooth)
+    smooth <- gratia::get_smooths_by_id(model, id)[[1L]]
+    vars <- gratia::smooth_variable(smooth)
+    by_var <- gratia::by_variable(smooth)
     if (!identical(by_var, "NA")) {
       vars <- append(vars, by_var)
     }