terra (version 1.3-22)

predict: Spatial model predictions

Description

Make a SpatRaster object with predictions from a fitted model object (for example, obtained with glm or randomForest). The first argument is a SpatRaster object with the predictor variables. The names in the Raster object should exactly match those expected by the model. Any regression like model for which a predict method has been implemented (or can be implemented) can be used.

This approach of using model predictions is commonly used in remote sensing (for the classification of satellite images) and in ecology, for species distribution modeling.

Usage

# S4 method for SpatRaster
predict(object, model, fun=predict, ..., factors=NULL, const=NULL, na.rm=FALSE,
          index=NULL, cores=1, cpkgs=NULL, filename="", overwrite=FALSE, wopt=list())

Arguments

object

SpatRaster

model

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

fun

function. The predict function that takes model as first argument. The default value is predict, but can be replaced with e.g. predict.se (depending on the type of model), or your own custom function

...

additional arguments for fun

const

data.frame. Can be used to add a constant value as a predictor variable so that you do not need to make a SpatRaster layer for it

factors

list with levels for factor variables. The list elements should be named with names that correspond to names in object such that they can be matched. This argument may be omitted for standard models such as "glm" as the predict function will extract the levels from the model object, but it is necessary in some other cases (e.g. cforest models from the party package)

na.rm

logical. If TRUE, cells with NA values in the predictors are removed from the computation. 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 model that returns predicted values even if some (or all!) variables are NA

index

integer. To select subset of output variables

cores

positive integer. If cores > 1, a 'parallel' package cluster with that many cores is created and used

cpkgs

character. The package(s) that need to be loaded on the nodes to be able to run the model.predict function (see examples)

filename

character. Output filename

overwrite

logical. If TRUE, filename is overwritten

wopt

list with named options for writing files as in writeRaster

Value

SpatRaster

Examples

# NOT RUN {
logo <- rast(system.file("ex/logo.tif", package="terra"))   
names(logo) <- c("red", "green", "blue")
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 <- matrix(c(22, 33, 64, 85, 92, 94, 59, 27, 30, 64, 60, 33, 31, 9,
   99, 67, 15, 5, 4, 30, 8, 37, 42, 27, 19, 69, 60, 73, 3, 5, 21,
   37, 52, 70, 74, 9, 13, 4, 17, 47), ncol=2)

xy <- rbind(cbind(1, p), cbind(0, a))

# extract predictor values for points
e <- extract(logo, xy[,2:3])

# combine with response (excluding the ID column)
v <- data.frame(cbind(pa=xy[,1], e[,-1]))

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

#predict to a raster
r1 <- predict(logo, model)

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

# logistic regression
model <- glm(formula=pa~., data=v, family="binomial")
r1log <- predict(logo, model, type="response")

# use a modified function to get the probability and standard error
# 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))
}

r2 <- predict(logo, model, fun=predfun)

# principal components of a SpatRaster
# here using sampling to simulate an object too large
# to feed all its values to prcomp

sr <- values(spatSample(logo, 100, as.raster=TRUE))
pca <- prcomp(sr)

x <- predict(logo, pca)
plot(x)

## parallelization
# }
# NOT RUN {
## simple case with GLM 
model <- glm(formula=pa~., data=v)
p <- predict(logo, model, cores=2)

## The above does not work with a model from a contributed
## package, as the package needs to be loaded in each core. 
## Below are three approaches to deal with that 

library(randomForest)
rfm <- randomForest(formula=pa~., data=v)

## approach 0 (not parallel) 
rp0 <- predict(logo, rfm)

## approach 1, use the "cpkgs" argument 
rp1 <- predict(logo, rfm, cores=2, cpkgs="randomForest")

## approach 2, write a custom predict function that loads the package
rfun <- function(mod, dat, ...) {
	library(randomForest)
	predict(mod, dat, ...)
}
rp2 <- predict(logo, rfm, fun=rfun, cores=2)

## approach 3, write a parallelized custom predict function 
rfun <- function(mod, dat, ...) {
	ncls <- length(cls)
	nr <- nrow(dat)
	s <- split(dat, rep(1:ncls, each=ceiling(nr/ncls), length.out=nr))
	unlist(  parallel::clusterApply(cls, s, function(x, ...) predict(mod, x, ...))  )
}

library(parallel)
cls <- parallel::makeCluster(2)
parallel::clusterExport(cls, c("rfm", "rfun", "randomForest"))
rp3 <- predict(logo, rfm, fun=rfun)
parallel::stopCluster(cls)

plot(c(rp0, rp1, rp2, rp3))
# }
# NOT RUN {
# }