# predict

0th

Percentile

##### Distribution model predictions

Make a RasterLayer with a prediction based on a model object of class the inherits from 'DistModel', including: Bioclim, Domain, MaxEnt, Mahalanobis, and GeographicDistance. Predictions with model objects that do not inherit from DistModel can be made using the similar predict function in the 'raster' package.

Provide a Raster* object with the independent variables. The names of the layers in the Raster* object should include those expected by the model.

Keywords
methods, spatial
##### Value

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

##### Methods

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" and "windows" (on that platform only) ... Additional model specific arguments. And additional arguments for file writing as for writeRaster

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.

For geoDist models, there is an additional argument fun that allows you to use your own (inverse) distance function, and argument scale=1 that allows you to scale the values (distances smaller than this value become one, and the others are divided by this value before computing the inverse distance).

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, geoDist, convHull

Extent object: extent

##### Aliases
• predict
• predict,Bioclim-method
• predict,Domain-method
• predict,Mahalanobis-method
• predict,MaxEnt-method
• predict,MaxEntReplicates-method
• predict,ConvexHull-method
• predict,CircleHull-method
• predict,RectangularHull-method
• predict,CirclesRange-method
• predict,GeographicDistance-method
• predict,InvDistWeightModel-method
• predict,VoronoiHull-method
##### Examples
# NOT RUN {
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)
plot(p)
# }

Documentation reproduced from package dismo, version 1.1-4, License: GPL (>= 3)

### Community examples

Looks like there are no examples yet.