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Including offsets is another option to integrate spatial prior information in linear and additive regression models. Offsets shift the intercept of the regression fit by a certain amount. Although only one offset can be added to a regression model, it is possible to combine several spatial-explicit estimates into one offset by calculating the sum of all spatial-explicit layers.

Usage

add_offset(x, layer, add = TRUE)

# S4 method for BiodiversityDistribution,SpatRaster,logical
add_offset(x,layer,add)

# S4 method for BiodiversityDistribution,sf,logical
add_offset(x,layer,add)

Arguments

x

distribution() (i.e. BiodiversityDistribution) object.

layer

A sf or SpatRaster object with the range for the target feature.

add

logical specifying whether new offset is to be added. Setting this parameter to FALSE replaces the current offsets with the new one (Default: TRUE).

Value

Adds an offset to a distribution object.

Details

This function allows to set any specific offset to a regression model. The offset has to be provided as spatial SpatRaster object. This function simply adds the layer to a distribution() object. Note that any transformation of the offset (such as log) has do be done externally!

If the layer is range and requires additional formatting, consider using the function add_offset_range() which has additional functionalities such such distance transformations.

Note

Since offsets only make sense for linear regressions (and not for instance regression tree based methods such as engine_bart), they do not work for all engines. Offsets specified for non-supported engines are ignored during the estimation

References

  • Merow, C., Allen, J.M., Aiello-Lammens, M., Silander, J.A., 2016. Improving niche and range estimates with Maxent and point process models by integrating spatially explicit information. Glob. Ecol. Biogeogr. 25, 1022–1036. https://doi.org/10.1111/geb.12453

Examples

if (FALSE) {
 x <- distribution(background) |>
   add_predictors(covariates) |>
   add_offset(nicheEstimate)
}