library(raster)
library(randomForest)

set.seed(123)

r <- raster(nrows = 10, ncols = 20)
r1 <- r
r1[] <- rnorm(200, mean = 0, sd = 0.5)
r2 <- r
r2[] <- rnorm(200, mean = 0, sd = 1)
r3 <- r
r3[] <- rnorm(200, mean = 2, sd = 0.5)
r4 <- r
r4[] <- rnorm(200, mean = 1, sd = 1)

s <- stack(r1,r2,r3,r4)

names(s) <- c('x','y','z','w')

train <- raster::sampleRandom(s,30)

rf <- randomForest(x ~ y + z + w, data = train)

prd <- predict(rf, newdata = as.data.frame(s[[2:4]]))

r[] <- prd

plot(cbind(as.data.frame(r), as.data.frame(r1)), xlab = 'observed', ylab = 'predicted')