predict: Spatial model predictions

Description

Make a Raster* object with predictions based on a a fitted model object. The first argument is a Raster* object with the independent variables. The layerNames in the RasterStack should exactely match those expected by the model. This will be the case if the same Raster* object was used (via extract) 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 (or can be implemented) can be used.

Usage

predict(object, ...)

Arguments

object

a RasterStack, RasterBrick or RasterLayer object

...

Additional arguments. See under Methods

Value

RasterLayer or RasterBrick

Methods

predict(object, model, filename='', fun=predict, ext=NULL, const=NULL, index=1, na.rm=TRUE, ...) rll{ object a Raster* object. Typcially a multi-layer type (RasterStack or RasterBrick) model A fitted model of any class that has a 'predict' method (or for which you can supply a similar method as fun argument. E.g. glm, gam, or randomForest filename Optional output filename fun Function. Default value is 'predict', but can be replaced with e.g. predict.se (depending on the type of model), or your own custom function. 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 for which it is currently not possible to make a RasterLayer index Integer. To select the column if predict.'model' returns a matrix with multiple columns na.rm Logical. Remove cells with NA values in the predictors before solving the model (and return a NA value for those cells). This option prevents errors with models that cannot handle NA values. In most other cases this will not affect the output. An exception is when predicting with a boosted regression trees model because these return predicted value even if some (or all!) variables are NA ... Additional arguments to pass to the predict.'model' function } The argument se.fit has been removed. See the examples on how to get a prediction of se.fit of a glm model. 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. "text", "window", or "" (the default, no progress bar) }

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
# see the dismo package for more of that.

# create a RasterStack or RasterBrick with with a set of predictor layers
logo <- brick(system.file("external/rlogo.grd", package="raster"))
layerNames(logo)

# the predictor variables
par(mfrow=c(2,2))
plotRGB(logo, main='logo')
plot(logo, 1, col=rgb(cbind(0:255,0,0), maxColorValue=255))
plot(logo, 2, col=rgb(cbind(0,0:255,0), maxColorValue=255))
plot(logo, 3, col=rgb(cbind(0,0,0:255), maxColorValue=255))
par(mfrow=c(1,1))

#get presence and pseudo-absence points
p <- 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)
# 
a <- cbind(runif(250)*(xmax(logo)-xmin(logo))+xmin(logo), runif(250)*(ymax(logo)-ymin(logo))+ymin(logo))

#extract values for points from stack
xy <- rbind(cbind(1, p), cbind(0, a))
v <- data.frame(cbind(xy[,1], extract(logo, xy[,2:3])))
colnames(v)[1] <- 'pa'

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

#predict to a raster
r1 <- predict(logo, model, progress='window')

plot(r1)
points(p, bg='blue', pch=21)
points(a, bg='red', pch=21)

# use a modified function to get a RasterBrick with p and se
# from the glm model. The values returned by 'predict' are in a list,
# and this list needs to be transformed to a matrix

predfun <- function(model, data) {
  v <- predict(model, data, se.fit=TRUE)
  cbind(p=as.vector(v$fit), se=as.vector(v$se.fit))
}

# predfun returns two variables, so use index=1:2
r2 <- predict(logo, model, fun=predfun, index=1:2)

# principal components of a RasterBrick
# here using sampling to simulate an object too large
# too feed all its values to prcomp
v <- sampleRandom(logo, 100)
pca <- prcomp(v)

# note the use of the 'index' argument
x <- predict(logo, pca, index=1:3)
plot(x)

require(randomForest)
rfmod <- randomForest(pa ~., data=v)

## note the additional argument "type='response'" that is passed to predict.randomForest
r3 <- predict(logo, rfmod, type='response', progress='window')

## get a RasterBrick with class membership probabilities
v$pa <- as.factor(v$pa)
rfmod2 <- randomForest(pa ~., data=v)
r4 <- predict(logo, rfmod2, type='prob', index=1:2)
spplot(r4)