View source: R/sdm_summarize.R
sdm_summarize | R Documentation |
Merge model performance tables
sdm_summarize(models)
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 |
Combined model performance table for all input models. Models fit with tune will include model performance for the best hyperparameters.
## Not run:
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
## End(Not run)
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