Merge model performance tables

Usage

sdm_summarize(models)

Arguments

models: list of one or more models fitted with fit_ or tune_ functions, or a fit_ensemble output, a esm_ family function output. A list a single or several models fitted with some of fit_ or tune_ functions or object returned by the fit_ensemble function. Usage models = list(mod1, mod2, mod3)

Value

Combined model performance table for all input models. Models fit with tune will include model performance for the best hyperparameters.

Examples

if (FALSE) { # \dontrun{
data(abies)
abies

# In this example we will partition the data using the k-fold method

abies2 <- part_random(
  data = abies,
  pr_ab = "pr_ab",
  method = c(method = "kfold", folds = 5)
)

# Build a generalized additive model using fit_gam

gam_t1 <- fit_gam(
  data = abies2,
  response = "pr_ab",
  predictors = c("aet", "ppt_jja", "pH", "awc", "depth"),
  predictors_f = c("landform"),
  partition = ".part",
  thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen")
)

gam_t1$performance

# Build a generalized linear model using fit_glm

glm_t1 <- fit_glm(
  data = abies2,
  response = "pr_ab",
  predictors = c("aet", "ppt_jja", "pH", "awc", "depth"),
  predictors_f = c("landform"),
  partition = ".part",
  thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
  poly = 0,
  inter_order = 0
)

glm_t1$performance

# Build a tuned random forest model using tune_raf

tune_grid <-
  expand.grid(
    mtry = seq(1, 7, 1),
    ntree = c(300, 500, 700)
  )

rf_t1 <-
  tune_raf(
    data = abies2,
    response = "pr_ab",
    predictors = c(
      "aet", "cwd", "tmin", "ppt_djf",
      "ppt_jja", "pH", "awc", "depth"
    ),
    predictors_f = c("landform"),
    partition = ".part",
    grid = tune_grid,
    thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
    metric = "TSS",
  )

rf_t1$performance

# Merge sdm performance tables

merge_df <- sdm_summarize(models = list(gam_t1, glm_t1, rf_t1))

merge_df
} # }