clamp.vars: Clamp predictor variables

View source: R/utilities.R

clamp.varsR Documentation

Clamp predictor variables


This function restricts the values of one or more predictor variable rasters to stay within the bounds of the input occurrence and background data (argument "ref.vals"). This is termed "clamping", and is mainly used to avoid making extreme extrapolations when making model predictions to environmental conditions outside the range of the occurrence / background data used to train the model. Clamping can be done on variables of choice on one or both tails of their distributions (i.e., arguments "left" and "right" for minimum and maximum clamps, respectively). If "left" and/or "right" are not specified and left at the default NULL, the function will clamp all variables for that tail (thus, the function default is to clamp all variables on both sides). To turn off clamping for one side, enter "none" for either "left" or "right".

Categorical variables need to be declared with the argument "categoricals". These variables are excluded from the clamping analysis, but are put back into the RasterStack that is returned.


clamp.vars(orig.vals, ref.vals, left = NULL, right = NULL, categoricals = NULL)



RasterStack / matrix / data frame: environmental predictor variables (must be in same geographic projection as occurrence data), or predictor variables values for the original records


matrix / data frame: predictor variable values for the reference records (not including coordinates), used to determine the minimums and maximums – this should ideally be the occurrences + background (can be made with raster::extract())


character vector: names of variables to get a minimum clamp; can be "none" to turn off minimum clamping


character vector: names of variables to get a maximum clamp, can be "none" to turn off maximum clamping


character vector: name or names of categorical environmental variables


The clamped Raster* object.


Stephen J. Phillips, Jamie M. Kass, Gonzalo Pinilla-Buitrago

ENMeval documentation built on Jan. 9, 2023, 5:08 p.m.