more informative error for workflow_set that was not fitted

R/add_member.R

@@ -23,7 +23,7 @@ add_member <- function(x, member, ...) {
 #' @rdname add_member
 #' @export
 add_member.default <- function(x, member, ...) {
-  stop("no method available for this object type")
+  stop("no method available for this object type: ", class(member))
 }
 
 #' @param id the name to be given to this workflow in the `wflow_id` column.
@@ -88,6 +88,10 @@ add_member.tune_results <- function(x, member, metric = NULL, id = NULL, ...) {
 add_member.workflow_set <- function(x, member, metric = NULL, ...) {
   for (i_wflow in member$wflow_id) {
     this_res <- workflowsets::extract_workflow_set_result(member, id = i_wflow)
+    # if the result is an empty list, throw an error (how did we get to such a situation?)
+    if (length(this_res)==0) {
+      stop("no result found for workflow ", i_wflow, "; did you forget to fit the workflow?")
+    }
     x <- x %>% add_member(this_res, metric = metric, id = i_wflow)
   }
   x

R/sdm_spec_gam.R

@@ -2,7 +2,19 @@
 #'
 #' This function returns a [parsnip::model_spec] for a General Additive Model to
 #' be used as a classifier of presences and absences in Species Distribution Model.
-#'
+#' 
+#' Note that, when using GAMs in a `workflow_set()`, it is necessary to
+#' update the model with [gam_formula()] (see [`parsnip::model_formula`] for a
+#' discussion of formulas with special terms in `tidymodels`):
+#' \preformatted{
+#' workflow_set(
+#'   preproc = list(default = my_recipe),
+#'   models = list(gam = sdm_spec_gam()),
+#'   cross = TRUE
+#' ) \%>\% update_workflow_model("default_gam",
+#'                             spec = sdm_spec_gam(),
+#'                             formula = gam_formula(my_recipe))
+#' }
 #' @param ... parameters to be passed to [parsnip::gen_additive_mod()] to
 #' customise the model. See the help of that function for details.
 #' @param tune character defining the tuning strategy. As there are no hyperparameters
@@ -14,6 +26,7 @@
 #' my_gam_spec <- sdm_spec_gam()
 #' @family "sdm model specifications"
 #' @export
+#' @seealso [parsnip::gen_additive_mod()] [gam_formula()]
 
 sdm_spec_gam <- function(..., tune = "none") {
   tune <- rlang::arg_match(tune)

data-raw/restructure/a_tidysdm_workflow_options.Rmd

@@ -180,7 +180,7 @@ To build a `workflow_set` of different models, the `hyperparameters` we want to
 For the most commonly used models, `tidysdm` automatically chooses the most important parameters:\
 - Generalised Linear Model, using `sdm_spec_glm()`,
 
-\- General Additive Model, using `sdm_spec_gam()`,
+\- General Additive Model, using `sdm_spec_gam()` (note that because of the non-standard formula interface of `mgcv`, it is necessary to update the model with `gam_formula()`),
 
 \- random forest specs with tuning, using `sdm_spec_rf()`,
 

vignettes/a0_tidysdm_overview.Rmd

@@ -428,7 +428,10 @@ tutorial](https://workflowsets.tidymodels.org/articles/tuning-and-comparing-mode
 The latter three models have tunable hyperparameters. For the most
 commonly used models, `tidysdm` automatically chooses the most important
 parameters, but it is possible to fully customise model specifications
-(e.g. see the help for `sdm_spec_rf`).
+(e.g. see the help for `sdm_spec_rf`). Note that, if you used GAMs with `sdm_spec_gam()`,
+it is necessary to update the model with `gam_formula()` due to the non-standard
+formula notation of GAMs (see the help
+of `sdm_spec_gam()` for an example of how to do this).
 
 ```{r workflow_set}
 lacerta_models <-