tidyverse-methods: Tidyverse methods for caretSDM objects

In caretSDM: Build Species Distribution Modeling using 'caret'

Tidyverse methods for caretSDM objects

Description

Set of functions to facilitate the use of caretSDM through tidyverse grammatics.

Usage

select_predictors(x, ...)

## S3 method for class 'sdm_area'
select(.data, ...)

## S3 method for class 'input_sdm'
select(.data, ...)

## S3 method for class 'sdm_area'
mutate(.data, ...)

## S3 method for class 'input_sdm'
mutate(.data, ...)

## S3 method for class 'sdm_area'
filter(.data, ..., .by, .preserve)

## S3 method for class 'input_sdm'
filter(.data, ..., .by, .preserve)

## S3 method for class 'occurrences'
filter(.data, ..., .by, .preserve)

filter_species(x, spp = NULL, ...)

Arguments

x	sdm_area or input_sdm object.
...	character arguments to pass to the given function.
.data	Data to pass to tidyr function.
.by	See ?dplyr::filter.
.preserve	See ?dplyr::filter.
spp	Species to be filtered.

Value

The transformed sdm_area/input_sdm object.

Examples

# Create sdm_area object:
sa <- sdm_area(parana, cell_size = 25000, crs = 6933)

# Include predictors:
sa <- add_predictors(sa, bioc) |> select_predictors(c("bio1", "bio4", "bio12"))

caretSDM documentation built on Aug. 29, 2025, 5:17 p.m.