predict: Spatial model predictions

Description

Make a RasterLayer with a prediction based on a a fitted model object. Provide a Raster* with the independent variables. The layerNames in the RasterStack should exactely match those expected by the model. This will be the case if the RasterStack was used (via xyValues) to obtain the values to fit the model (see the example). Any type of model (e.g. glm. gam, randomforest) for which a predict method has been implemented can be used.

Usage

predict(object, ...)

Arguments

object

a RasterStack, RasterBrick or RasterLayer object

...

Additional arguments. See under Methods

Value

a RasterLayer object

Methods

predict(object, model, filename='', ext=NULL, const=NULL, index=1, se.fit=FALSE, ...) rll{ object a RasterStack, RasterBrick or RasterLayer object model A fitted model 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 ext An Extent object to limit the prediction to a sub-region of x const data.frame. Can be used to add a constant for which there is no Raster object for model predictions. Particulalry useful if the constant is a character-like factor value index Integer. To select the column if predict.'model' returns a matrix with multiple columns se.fit Extract the standard error rather then the prediction (works for GLM and perhaps other methods where the prediction and se.fit are returned as a list) ... Additional arguments to pass to the predict.'model' function } The following additional arguments can be passed, to replace default values 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 progress Character. Valid values are "text", "tcltk", "windows" (on that platform only) and "" }

Examples

Run this code

# A simple model to predict the location of the R in the R-logo using 20 presence points 
# and 50 (random) pseudo-absence points. This type of model is often used to predict species distributions

# create a RasterStack (a set of predictor rasters)
logo <- stack(system.file("pictures/Rlogo.jpg", package="rgdal"))
layerNames(logo) <- c('red', 'green', 'blue')

#get presence and absence points
presence <- 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)
# random absence
absence <- cbind(runif(50)*(xmax(logo)-xmin(logo))+xmin(logo), runif(50)*(ymax(logo)-ymin(logo))+ymin(logo))

#extract values for points from stack
xy <- rbind(cbind(1, presence), cbind(0, absence))
v <- cbind(xy[,1], xyValues(logo, xy[,2:3]))
colnames(v)[1] <- 'presabs'

#build a model, here an example with glm 
model <- glm(formula=presabs~., data=data.frame(v))

#predict to a raster
r <- predict(logo, model, progress='text')

plot(r>0.3)
points(presence, bg='blue', pch=21)
points(absence, bg='red', pch=21)

## also try:
# require(randomForest)
## formula <- as.factor(presabs) ~.
# formula <- presabs ~.
# model <- randomForest(formula, data=data.frame(v))
# r2 <- predict(logo, type='response', model, progress='text')
## note the additional argument "type='response'" that is passed to predict.randomForest

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