Setting maxent parameters manually within TidySDM Package in R

Question

Within the TidySDM package, you can define what models you wish to include in your ensemble with the "workflow_set" function. When including a maxent model you can use the argument "sdm_spec_maxent" and set the "tune" parameter to sdm, all, custom, or none. I wish to use the custom tune setting and manually set the feature class and regularization multiplier options. I wish to provide a range of values which I have based on the help file Maxent_params. However I receive an error each time that says, "the condition has length > 1".

Here is the exact code I am using for the workflow set, as based on the tidySDM github overview example and the help files.

dive_models <- workflow_set(
  preproc = list(default = dive.rec), 
  models = list(
    glm = sdm_spec_glm(), 
    gbm = sdm_spec_boost_tree(
      tune = "sdm"
    ), 
    maxent = maxent(
      mode = "classification", 
      engine = "maxnet", 
      feature_classes(values = c("L","Q", "H", "T", "LQ", "LH", "LT", "QH", "QT",     "HT", "LQH", "LQT", "QHT", "LHT", "LQHT")),
      regularization_multiplier(range = c(0.5, 3), trans = NULL)
    ), 
    mars = mars(
      mode = "classification", 
      engine = "earth", 
      prod_degree = 4
    )
  ), 
  cross = T
) |> 
  option_add(control = control_ensemble_grid())

Does anyone see what i may be missing since I have formatted this exactly as it is in the help file?

Answer 1

To use a custom range for tuning parameters, you need to create a custom object with parameters. Here is a reprex changing the two parameters for maxent:

library(tidysdm)
#> Loading required package: tidymodels
#> Loading required package: spatialsample
lacerta_thin <- terra::readRDS(system.file("extdata/lacerta_thin_all_vars.rds",
                                                             package = "tidysdm"))
# create a recipe
lacerta_rec <- recipe(lacerta_thin, formula = class ~ .)
# set up custom parameters ranges for maxent
maxent_param <- parameters(regularization_multiplier(),feature_classes()) %>% 
  update(regularization_multiplier = regularization_multiplier(c(0.1,0.5))) %>%
  update(feature_classes = feature_classes(c("l","h")))
# create the workflow set
lacerta_models <-
  workflow_set(
    preproc = list(default = lacerta_rec),
    models = list(
      # the standard glm specs
      glm = sdm_spec_glm(),
      # maxent specs with tuning
      maxent = maxent(
        mode = "classification", 
        engine = "maxnet",
        regularization_multiplier = tune(),
        feature_classes = tune()
      )
    ),
    # make all combinations of preproc and models,
    cross = TRUE
  ) %>%
  # tweak controls to store information needed later to create the ensemble
  option_add(control = control_ensemble_grid()) %>%
  # add the custom maxent parameter ranges
  option_add(param_info = maxent_param,id="default_maxent")
# spatial block
lacerta_cv <- spatial_block_cv(data = lacerta_thin, v = 3, n = 5)
# tune the models
set.seed(1234567)
lacerta_models <-
  lacerta_models %>%
  workflow_map("tune_grid",
               resamples = lacerta_cv, grid = 3,
               metrics = sdm_metric_set(), verbose = TRUE
  )
#> i    No tuning parameters. `fit_resamples()` will be attempted
#> i 1 of 2 resampling: default_glm
#> → A | warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> There were issues with some computations   A: x1
#> There were issues with some computations   A: x1
#> 
#> ✔ 1 of 2 resampling: default_glm (128ms)
#> i 2 of 2 tuning:     default_maxent
#> ✔ 2 of 2 tuning:     default_maxent (1.2s)
# collect the results for the maxent model
workflowsets::extract_workflow_set_result(lacerta_models,id="default_maxent") %>% collect_metrics()
#> # A tibble: 9 × 8
#>   regularization_multip…¹ feature_classes .metric .estimator  mean     n std_err
#>                     <dbl> <chr>           <chr>   <chr>      <dbl> <int>   <dbl>
#> 1                   0.230 h               boyce_… binary     0.317     3  0.269 
#> 2                   0.230 h               roc_auc binary     0.812     3  0.0356
#> 3                   0.230 h               tss_max binary     0.589     3  0.0513
#> 4                   0.252 l               boyce_… binary     0.533     3  0.139 
#> 5                   0.252 l               roc_auc binary     0.817     3  0.0421
#> 6                   0.252 l               tss_max binary     0.594     3  0.0865
#> 7                   0.451 l               boyce_… binary     0.614     3  0.0578
#> 8                   0.451 l               roc_auc binary     0.815     3  0.0423
#> 9                   0.451 l               tss_max binary     0.601     3  0.0937
#> # ℹ abbreviated name: ¹​regularization_multiplier
#> # ℹ 1 more variable: .config <chr>

^{Created on 2025-02-13 with reprex v2.1.1}