Add predictors to sdm_area — add_predictors • caretSDM

This function includes new predictors to the sdm_area object.

Usage

add_predictors(sa, pred, variables_selected = NULL, gdal = TRUE,
                      lines_as_sdm_area = FALSE)

get_predictors(i)

Arguments

sa: A sdm_area object.
pred: RasterStack, SpatRaster, stars or sf object with predictors data.
variables_selected: character vector with variables names in pred to be used as predictors. If NULL adds all variables.
gdal: Boolean. Force the use or not of GDAL when available. See details.
lines_as_sdm_area: Boolean. If x is a sf with LINESTRING geometry, it can be used to model species distribution in lines and not grid cells.
i: input_sdm or sdm_area object to retrieve data from.

Value

For add_predictors the same input sdm_area object is returned including the pred data binded to the previous grid. get_predictors retrieves the grid from the i object.

Details

add_predictors returns a sdm_area object with a grid built upon the x parameter. There are two ways to make the grid and resample the variables in sdm_area: with and without gdal. As standard, if gdal is available in you machine it will be used (gdal = TRUE), otherwise sf/stars will be used. lines_as_sdm_area and gdal parameters are passed to sdm_area function, so they will be used in the grid creation and resampling of predictors. They will be retrieved automatically from the sdm_area object.

Author

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

Examples

# Create sdm_area object:
sa <- sdm_area(parana, cell_size = 25000, 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)
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.

# Retrieve predictors data:
get_predictors(sa)
#> Simple feature collection with 373 features and 8 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -5276744 ymin: -3295037 xmax: -4626744 ymax: -2795037
#> Projected CRS: WGS 84 / NSIDC EASE-Grid 2.0 Global
#> First 10 features:
#>    cell_id GID0 CODIGOIB1 NOMEUF2 SIGLAUF3     bio1     bio4 bio12
#> 1        6   19        41       0        0 22.39812 262.4270  1268
#> 2        7   19        41       0        0 22.39812 262.4270  1268
#> 3        8   19        41       0        0 22.24774 259.8303  1243
#> 4        9   19        41       0        0 22.13017 260.5331  1230
#> 5       10   19        41       0        0 22.08299 260.9101  1203
#> 6       11   19        41       0        0 22.06016 259.2609  1192
#> 7       12   19        41       0        0 22.24014 258.5582  1234
#> 8       13   19        41       0        0 22.28710 257.0163  1236
#> 9       14   19        41       0        0 22.29775 258.6638  1247
#> 10      31   19        41       0        0 22.15600 267.6054  1317
#>                          geometry
#> 1  POLYGON ((-5151744 -2795037...
#> 2  POLYGON ((-5126744 -2795037...
#> 3  POLYGON ((-5101744 -2795037...
#> 4  POLYGON ((-5076744 -2795037...
#> 5  POLYGON ((-5051744 -2795037...
#> 6  POLYGON ((-5026744 -2795037...
#> 7  POLYGON ((-5001744 -2795037...
#> 8  POLYGON ((-4976744 -2795037...
#> 9  POLYGON ((-4951744 -2795037...
#> 10 POLYGON ((-5176744 -2820037...

Add predictors to `sdm_area`