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Model Response to Variables

Source: R/pdp_sdm.R
pdp_sdm.Rd

Obtain the Partial Dependence Plots (PDP) to each variable.

Usage

pdp_sdm(i, spp = NULL, algo = NULL, variables_selected = NULL, mean.only = FALSE)

get_pdp_sdm(i, spp = NULL, algo = NULL, variables_selected = NULL)

Arguments

i

A input_sdm object.

spp

A character vector with species names to obtain the PDPs. If NULL (standard), the first species in species_names(i) is used.

algo

A character containing the algorithm to obtain the PDP. If NULL (standard) all algorithms are mixed.

variables_selected

A character. If there is a subset of predictors that should be ploted in this, it can be informed using this parameter.

mean.only

Boolean. Should only the mean curve be plotted or a curve to each run should be included? Standard is FALSE.

Value

A plot (for pdp_sdm) or a data.frame (for get_pdp_sdm) with PDP values.

See also

varImp_sdm

Author

Luíz Fernando Esser (luizesser@gmail.com) https://luizfesser.wordpress.com

Examples

# Create sdm_area object:
sa <- sdm_area(parana, cell_size = 100000, output_crs = 6933)
#> ! Making grid over study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.

# Include predictors:
sa <- add_predictors(sa, bioc) |> select_predictors(c("bio1", "bio12"))
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.

# Include scenarios:
sa <- add_scenarios(sa)

# Create occurrences:
oc <- occurrences_sdm(occ, occ_crs = 6933)

# Create input_sdm:
i <- input_sdm(oc, sa)

# Pseudoabsence generation:
i <- pseudoabsences(i, method = "random", n_set = 3)

# Custom trainControl:
ctrl_sdm <- caret::trainControl(
  method = "repeatedcv",
  number = 2,
  repeats = 1,
  classProbs = TRUE,
  returnResamp = "all",
  summaryFunction = summary_sdm,
  savePredictions = "all"
)
# Train models:
i <- train_sdm(i, algo = c("naive_bayes"), ctrl = ctrl_sdm)
#> Loading required package: ggplot2
#> Loading required package: lattice

# PDP plots:
pdp_sdm(i)
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

get_pdp_sdm(i)
#> $naive_bayes
#> # A tibble: 129 × 4
#>    id               yhat variable value
#>    <chr>           <dbl> <chr>    <dbl>
#>  1 naive_bayes_pa1 0.987 bio1      16.6
#>  2 naive_bayes_pa1 0.986 bio1      16.9
#>  3 naive_bayes_pa1 0.984 bio1      17.1
#>  4 naive_bayes_pa1 0.982 bio1      17.4
#>  5 naive_bayes_pa1 0.979 bio1      17.6
#>  6 naive_bayes_pa1 0.976 bio1      17.9
#>  7 naive_bayes_pa1 0.971 bio1      18.1
#>  8 naive_bayes_pa1 0.965 bio1      18.4
#>  9 naive_bayes_pa1 0.958 bio1      18.6
#> 10 naive_bayes_pa1 0.949 bio1      18.9
#> # ℹ 119 more rows
#>