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_bias(x, layer, add = TRUE, points = NULL)
# S4 method for class 'BiodiversityDistribution,SpatRaster'
add_offset_bias(x, layer, add = TRUE, points = NULL)Arguments
- x
- distribution()(i.e.- BiodiversityDistribution) object.
- layer
- A - sfor- SpatRasterobject with the range for the target feature.
- add
- logicalspecifying whether new offset is to be added. Setting this parameter to- FALSEreplaces the current offsets with the new one (Default:- TRUE).
- points
- An optional - sfobject 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).
Value
Adds a bias offset to a distribution object.
Details
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.
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 
See also
Other offset:
add_offset(),
add_offset_elevation(),
add_offset_range(),
rm_offset()
Examples
if (FALSE) { # \dontrun{
 x <- distribution(background) |>
   add_predictors(covariates) |>
   add_offset_bias(samplingBias)
} # }
