summary_sdm | R Documentation |
This function is used in caret::trainControl(summaryFunction=summary_sdm)
to calculate
performance metrics across resamples.
summary_sdm(data, lev = NULL, model = NULL, custom_fun=NULL)
summary_sdm_presence_only(data, lev, threshold)
validate_on_independent_data(model, data_independent, obs_col_name)
data |
A |
lev |
A |
model |
Models names taken from |
custom_fun |
A custom function to be applied in models (not yet implemented). |
threshold |
Threshold for presence-only models. |
data_independent |
independent data.frame to calculate metrics. |
obs_col_name |
The name of the column with observed values. |
See ?caret::defaultSummary
for more details and options to pass on
caret::trainControl
.
A input_sdm
or a predictions
object.
Luíz Fernando Esser (luizesser@gmail.com) https://luizfesser.wordpress.com
train_sdm
# Create sdm_area object:
sa <- sdm_area(parana, cell_size = 100000, crs = 6933)
# Include predictors:
sa <- add_predictors(sa, bioc) |> select_predictors(c("bio1", "bio12"))
# Include scenarios:
sa <- add_scenarios(sa)
# Create occurrences:
oc <- occurrences_sdm(occ, crs = 6933) |> join_area(sa)
# Create input_sdm:
i <- input_sdm(oc, sa)
# Pseudoabsence generation:
i <- pseudoabsences(i, method = "random")
# 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) |>
suppressWarnings()
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