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Predictors Names Managing

Source: R/names.R
predictor_names.Rd

This function manage predictors names in sdm_area objects.

Usage

predictors(x)

# S3 method for class 'sdm_area'
predictors(x)

# S3 method for class 'input_sdm'
predictors(x)

set_predictor_names(x, new_names)

# S3 method for class 'input_sdm'
set_predictor_names(x, new_names)

# S3 method for class 'sdm_area'
set_predictor_names(x, new_names)

get_predictor_names(x)

# S3 method for class 'sdm_area'
get_predictor_names(x)

# S3 method for class 'input_sdm'
get_predictor_names(x)

test_variables_names(sa, scen)

set_variables_names(s1 = NULL, s2 = NULL, new_names = NULL)

Arguments

x

A sdm_area or input_sdm object to get/set predictors names.

new_names

A character vector from size length(get_predictor_names(x))

sa

A sdm_area object.

scen

A stars object with scenarios.

s1

A stars object with scenarios.

s2

A stars object with scenarios or a sdm_area object.

Value

predictors and get_predictor_names return a character vector with predictors names. test_variables_names returns a logical informing if all variables are equal in both objects (TRUE) or not (FALSE). set_variables_names returns the s1 object with new names provided by s2 or new_names.

Details

This functions is available so users can modify predictors names to better represent them. Use carefully to avoid giving wrong names to the predictors. Useful to make sure the predictors names are equal the names in scenarios. test_variables_names Tests if variables in a stars object (scen argument) matches the given sdm_area object (sa argument). set_variables_names will set s1 object variables names as the s2 object variables names OR assign new names to it.

See also

parana sdm_area

Author

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

Examples

# Create sdm_area object:
sa <- sdm_area(parana, cell_size = 50000, 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.

# Check predictors' names:
get_predictor_names(sa)
#> [1] "GID0"      "CODIGOIB1" "NOMEUF2"   "SIGLAUF3"  "bio1"      "bio4"     
#> [7] "bio12"