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
sf
orSpatRaster
object with the range for the target feature.- add
logical
specifying whether new offset is to be added. Setting this parameter toFALSE
replaces the current offsets with the new one (Default:TRUE
).- points
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
).
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)
} # }