Distribution model predictions

Make a RasterLayer with a prediction based on a model object of class Bioclim, Domain, MaxEnt, ConvexHull, or Mahalanobis. Provide a Raster* object with the independent variables. The layerNames in the Raster* object should include those expected by the model.

methods, spatial

  • A RasterLayer or, if x is a matrix), a vector.


predict(object, x, ext=NULL, filename='', progress='text', ...) rll{ object A fitted model of class Bioclim, Domain, MaxEnt, ConvexHull, or Mahalanobis (classes that inherit from DistModel) x A Raster* object or a data.frame ext An extent object to limit the prediction to a sub-region of x. Or an object that can be coerced to an Extent object by extent; such as a Raster* or Spatial* object filename Output filename for a new raster; if NA the result is not written to a file but returned with the RasterLayer object, in the data slot progress Character. Valid values are "" (no progress bar), "text", "tcltk", and "windows" (on that platform only) ... Additional model specific arguments and arguments for file writing (see below) } The following additional arguments can be passed, to replace default values, when writing the results to a file rll{ format Character. Output file type. See writeRaster datatype Character. Output data type; see dataType overwrite Logical. If TRUE, "filename" will be overwritten if it exists } For maxent models, there is an additional argument 'args' used to pass arguments (options) to the maxent software. See the help page for maxent for more information. For bioclim models, there is an additional argument 'tails' which you can use to ignore the left or right tail of the percentile distribution for a variable. If supplied, tails should be a character vector with a length equal to the number of variables used in the model. Valid values are "both" (the default), "low" and "high". For example, if you have a variable x with an observed distribution between 10 and 20 and you are predicting the bioclim value for a value 25, the default result would be zero (outside of all observed values); but if you use tail='low', the high (right) tail is ignored and the value returned will be 1.

See Also

For spatial predictions with GLM, GAM, BRT, randomForest, etc., see predict in the Raster package. To fit a model that can be used with this predict method, see maxent, bioclim, mahal, domain, convHull

  • predict
  • predict,Bioclim-method
  • predict,Domain-method
  • predict,Mahalanobis-method
  • predict,MaxEnt-method
  • predict,ConvexHull-method
logo <- stack(system.file("external/rlogo.grd", package="raster"))
pts <- matrix(c(48, 48, 48, 53, 50, 46, 54, 70, 84, 85, 74, 84, 95, 85, 66, 42, 26, 4, 19, 17, 7, 14, 26, 29, 39, 45, 51, 56, 46, 38, 31, 22, 34, 60, 70, 73, 63, 46, 43, 28), ncol=2)
b <- bioclim(logo, pts)
# prediction for a sub-region
e <- extent(30,90,20,60)
p <- predict(b, logo, progress='text', ext=e)
Documentation reproduced from package dismo, version 0.5-1, License: GPL (>= 3)

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