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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.


add_offset_bias(x, layer, add = TRUE, points = NULL)

# S4 method for BiodiversityDistribution,SpatRaster
add_offset_bias(x, layer, add = TRUE, points = NULL)



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


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


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


An optional sf object with key points. The location of the points are then used to calculate the probability that a cell has been sampled while accounting for area differences. (Default: NULL).


Adds a bias offset to a distribution object.


This functions emulates the use of the add_offset() function, however applies an inverse transformation to remove the provided layer from the overall offset. So if for instance a offset is already specified (such as area), this function removes the provided bias.layer from it via "offset(log(off.area)-log(bias.layer))"

Note that any transformation of the offset (such as log) has do be done externally!

If a generic offset is added, consider using the add_offset() function. If the layer is a expert-based range and requires additional parametrization, consider using the function add_offset_range() or the bossMaps R-package.


  • 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.

See also


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