```
# 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"))
names(logo)
if (FALSE) {
# 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))
}
# known presence and 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 <- 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)
# extract values for points
xy <- rbind(cbind(1, p), cbind(0, a))
v <- data.frame(cbind(pa=xy[,1], extract(logo, xy[,2:3])))
#build a model, here an example with glm
model <- glm(formula=pa~., data=v)
#predict to a raster
r1 <- predict(logo, model, progress='text')
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)
if (FALSE) {
# You can use multiple cores to speed up the predict function
# by calling it via the clusterR function (you may need to install the snow package)
beginCluster()
r1c <- clusterR(logo, predict, args=list(model))
r2c <- clusterR(logo, predict, args=list(model=model, fun=predfun, index=1:2))
}
# principal components of a RasterBrick
# here using sampling to simulate an object too large
# to feed all its values to prcomp
sr <- sampleRandom(logo, 100)
pca <- prcomp(sr)
# note the use of the 'index' argument
x <- predict(logo, pca, index=1:3)
plot(x)
if (FALSE) {
# partial least square regression
library(pls)
model <- plsr(formula=pa~., data=v)
# this returns an array:
predict(model, v[1:5,])
# write a function to turn that into a matrix
pfun <- function(x, data) {
y <- predict(x, data)
d <- dim(y)
dim(y) <- c(prod(d[1:2]), d[3])
y
}
pp <- predict(logo, model, fun=pfun, index=1:3)
# Random Forest
library(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
vv <- v
vv$pa <- as.factor(vv$pa)
rfmod2 <- randomForest(pa ~., data=vv)
r4 <- predict(logo, rfmod2, type='prob', index=1:2)
spplot(r4)
# cforest (other Random Forest implementation) example with factors argument
v$red <- as.factor(round(v$red/100))
logo$red <- round(logo[[1]]/100)
library(party)
m <- cforest(pa~., control=cforest_unbiased(mtry=3), data=v)
f <- list(levels(v$red))
names(f) <- 'red'
# the second argument in party:::predict.RandomForest
# is "OOB", and not "newdata" or similar. We need to write a wrapper
# predict function to deal with this
predfun <- function(m, d, ...) predict(m, newdata=d, ...)
pc <- predict(logo, m, OOB=TRUE, factors=f, fun=predfun)
# knn example, using calc instead of predict
library(class)
cl <- factor(c(rep(1, nrow(p)), rep(0, nrow(a))))
train <- extract(logo, rbind(p, a))
k <- calc(logo, function(x) as.integer(as.character(knn(train, x, cl))))
}
```

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