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combine <- function(...) {
pad0 <- function(x, len) c(x, rep(0, len-length(x)))
padm0 <- function(x, len) rbind(x, matrix(0, nrow=len-nrow(x),
ncol=ncol(x)))
rflist <- list(...)
areForest <- sapply(rflist, function(x) inherits(x, "randomForest"))
if (any(!areForest)) stop("Argument must be a list of randomForest objects")
## Use the first component as a template
rf <- rflist[[1]]
classRF <- rf$type == "classification"
trees <- sapply(rflist, function(x) x$ntree)
ntree <- sum(trees)
rf$ntree <- ntree
nforest <- length(rflist)
haveTest <- ! any(sapply(rflist, function(x) is.null(x$test)))
## Check if predictor variables are identical.
vlist <- lapply(rflist, function(x) rownames(importance(x)))
numvars <- sapply(vlist, length)
if (! all(numvars[1] == numvars[-1]))
stop("Unequal number of predictor variables in the randomForest objects.")
for (i in seq_along(vlist)) {
if (! all(vlist[[i]] == vlist[[1]]))
stop("Predictor variables are different in the randomForest objects.")
}
## Combine the forest component, if any
haveForest <- sapply(rflist, function(x) !is.null(x$forest))
if (all(haveForest)) {
nrnodes <- max(sapply(rflist, function(x) x$forest$nrnodes))
rf$forest$nrnodes <- nrnodes
rf$forest$ndbigtree <-
unlist(sapply(rflist, function(x) x$forest$ndbigtree))
rf$forest$nodestatus <-
do.call("cbind", lapply(rflist, function(x)
padm0(x$forest$nodestatus, nrnodes)))
rf$forest $bestvar <-
do.call("cbind",
lapply(rflist, function(x)
padm0(x$forest$bestvar, nrnodes)))
rf$forest$xbestsplit <-
do.call("cbind",
lapply(rflist, function(x)
padm0(x$forest$xbestsplit, nrnodes)))
rf$forest$nodepred <-
do.call("cbind", lapply(rflist, function(x)
padm0(x$forest$nodepred, nrnodes)))
tree.dim <- dim(rf$forest$treemap)
if (classRF) {
rf$forest$treemap <-
array(unlist(lapply(rflist, function(x) apply(x$forest$treemap, 2:3,
pad0, nrnodes))),
c(nrnodes, 2, ntree))
} else {
rf$forest$leftDaughter <-
do.call("cbind",
lapply(rflist, function(x)
padm0(x$forest$leftDaughter, nrnodes)))
rf$forest$rightDaughter <-
do.call("cbind",
lapply(rflist, function(x)
padm0(x$forest$rightDaughter, nrnodes)))
}
rf$forest$ntree <- ntree
if (classRF) rf$forest$cutoff <- rflist[[1]]$forest$cutoff
} else {
rf$forest <- NULL
}
if (classRF) {
## Combine the votes matrix:
rf$votes <- 0
rf$oob.times <- 0
areVotes <- all(sapply(rflist, function(x) any(x$votes > 1, na.rf=TRUE)))
if (areVotes) {
for(i in 1:nforest) {
rf$oob.times <- rf$oob.times + rflist[[i]]$oob.times
rf$votes <- rf$votes +
ifelse(is.na(rflist[[i]]$votes), 0, rflist[[i]]$votes)
}
} else {
for(i in 1:nforest) {
rf$oob.times <- rf$oob.times + rflist[[i]]$oob.times
rf$votes <- rf$votes +
ifelse(is.na(rflist[[i]]$votes), 0, rflist[[i]]$votes) *
rflist[[i]]$oob.times
}
rf$votes <- rf$votes / rf$oob.times
}
rf$predicted <- factor(colnames(rf$votes)[max.col(rf$votes)],
levels=levels(rf$predicted))
if(haveTest) {
rf$test$votes <- 0
if (any(rf$test$votes > 1)) {
for(i in 1:nforest)
rf$test$votes <- rf$test$votes + rflist[[i]]$test$votes
} else {
for (i in 1:nforest)
rf$test$votes <- rf$test$votes +
rflist[[i]]$test$votes * rflist[[i]]$ntree
}
rf$test$predicted <-
factor(colnames(rf$test$votes)[max.col(rf$test$votes)],
levels=levels(rf$test$predicted))
}
} else {
rf$predicted <- 0
for (i in 1:nforest) rf$predicted <- rf$predicted +
rflist[[i]]$predicted * rflist[[i]]$ntree
rf$predicted <- rf$predicted / ntree
if (haveTest) {
rf$test$predicted <- 0
for (i in 1:nforest) rf$test$predicted <- rf$test$predicted +
rflist[[i]]$test$predicted * rflist[[i]]$ntree
rf$test$predicted <- rf$test$predicted / ntree
}
}
## If variable importance is in all of them, compute the average
## (weighted by the number of trees in each forest)
have.imp <- !any(sapply(rflist, function(x) is.null(x$importance)))
if (have.imp) {
rf$importance <- rf$importanceSD <- 0
for(i in 1:nforest) {
rf$importance <- rf$importance +
rflist[[i]]$importance * rflist[[i]]$ntree
## Do the same thing with SD of importance, though that's not
## exactly right...
rf$importanceSD <- rf$importanceSD +
rflist[[i]]$importanceSD^2 * rflist[[i]]$ntree
}
rf$importance <- rf$importance / ntree
rf$importanceSD <- sqrt(rf$importanceSD / ntree)
haveCaseImp <- !any(sapply(rflist, function(x)
is.null(x$localImportance)))
## Average casewise importance
if (haveCaseImp) {
rf$localImportance <- 0
for (i in 1:nforest) {
rf$localImportance <- rf$localImportance +
rflist[[i]]$localImportance * rflist[[i]]$ntree
}
rf$localImportance <- rf$localImportance / ntree
}
}
## If proximity is in all of them, compute the average
## (weighted by the number of trees in each forest)
have.prox <- !any(sapply(rflist, function(x) is.null(x$proximity)))
if (have.prox) {
rf$proximity <- 0
for(i in 1:nforest)
rf$proximity <- rf$proximity + rflist[[i]]$proximity * rflist[[i]]$ntree
rf$proximity <- rf$proximity / ntree
}
## if there are inbag matrices, combine them as well.
hasInBag <- all(sapply(rflist, function(x) !is.null(x$inbag)))
if (hasInBag) rf$inbag <- do.call(cbind, lapply(rflist, "[[", "inbag"))
## Set confusion matrix and error rates to NULL
if (classRF) {
rf$confusion <- NULL
rf$err.rate <- NULL
if (haveTest) {
rf$test$confusion <- NULL
rf$err.rate <- NULL
}
} else {
rf$mse <- rf$rsq <- NULL
if (haveTest) rf$test$mse <- rf$test$rsq <- NULL
}
rf
}
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