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# tuning random forest over mtry parameter in parallel
library(foreach)
library(randomForest)
# a simple iterator over different values for the mtry argument
mtryiter <- function(from, to, stepFactor=2) {
nextEl <- function() {
if (from > to) stop('StopIteration')
i <- from
from <<- ceiling(from * stepFactor)
i
}
obj <- list(nextElem=nextEl)
class(obj) <- c('abstractiter', 'iter')
obj
}
# vector of ntree values that we're interested in
vntree <- c(25, 50, 100, 200, 500, 1000)
# function that gets random forest error information for different values of mtry
tune <- function(x, y, ntree=vntree, mtry=NULL, keep.forest=FALSE, ...) {
comb <- if (is.factor(y))
function(a, b) rbind(a, data.frame(ntree=ntree, mtry=b$mtry, error=b$err.rate[ntree, 1]))
else
function(a, b) rbind(a, data.frame(ntree=ntree, mtry=b$mtry, error=b$mse[ntree]))
foreach(mtry=mtryiter(1, ncol(x)), .combine=comb, .init=NULL,
.packages='randomForest') %dopar% {
randomForest(x, y, ntree=max(ntree), mtry=mtry, keep.forest=FALSE, ...)
}
}
# generate the inputs
x <- matrix(runif(2000), 100)
y <- gl(2, 50)
# execute randomForest
results <- tune(x, y)
# print the result
print(results)
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