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## mylevels() returns levels if given a factor, otherwise 0.
mylevels <- function(x) if (is.factor(x)) levels(x) else 0
"randomForest.default" <-
function(x, y=NULL, xtest=NULL, ytest=NULL, ntree=500,
mtry=if (!is.null(y) && !is.factor(y))
max(floor(ncol(x)/3), 1) else floor(sqrt(ncol(x))),
weights=NULL,
replace=TRUE, classwt=NULL, cutoff, strata,
sampsize = if (replace) nrow(x) else ceiling(.632*nrow(x)),
nodesize = if (!is.null(y) && !is.factor(y)) 5 else 1,
maxnodes=NULL,
importance=FALSE, localImp=FALSE, nPerm=1,
proximity, oob.prox=proximity,
norm.votes=TRUE, do.trace=FALSE,
keep.forest=!is.null(y) && is.null(xtest), corr.bias=FALSE,
keep.inbag=FALSE, ...) {
addclass <- is.null(y)
classRF <- addclass || is.factor(y)
if (!classRF && length(unique(y)) <= 5) {
warning("The response has five or fewer unique values. Are you sure you want to do regression?")
}
if (classRF && !addclass && length(unique(y)) < 2)
stop("Need at least two classes to do classification.")
n <- nrow(x)
p <- ncol(x)
if (n == 0) stop("data (x) has 0 rows")
x.row.names <- rownames(x)
x.col.names <- if (is.null(colnames(x))) 1:ncol(x) else colnames(x)
## overcome R's lazy evaluation:
keep.forest <- keep.forest
testdat <- !is.null(xtest)
if (testdat) {
if (ncol(x) != ncol(xtest))
stop("x and xtest must have same number of columns")
ntest <- nrow(xtest)
xts.row.names <- rownames(xtest)
}
## Make sure mtry is in reasonable range.
if (mtry < 1 || mtry > p)
warning("invalid mtry: reset to within valid range")
mtry <- max(1, min(p, round(mtry)))
if (!is.null(y)) {
if (length(y) != n) stop("length of response must be the same as predictors")
addclass <- FALSE
} else {
if (!addclass) addclass <- TRUE
y <- factor(c(rep(1, n), rep(2, n)))
x <- rbind(x, x)
}
## Check for NAs.
if (any(is.na(x))) stop("NA not permitted in predictors")
if (testdat && any(is.na(xtest))) stop("NA not permitted in xtest")
if (any(is.na(y))) stop("NA not permitted in response")
if (!is.null(ytest) && any(is.na(ytest))) stop("NA not permitted in ytest")
if (is.data.frame(x)) {
xlevels <- lapply(x, mylevels)
ncat <- sapply(xlevels, length)
## Treat ordered factors as numerics.
ncat <- ifelse(sapply(x, is.ordered), 1, ncat)
x <- data.matrix(x)
if(testdat) {
if(!is.data.frame(xtest))
stop("xtest must be data frame if x is")
xfactor <- which(sapply(xtest, is.factor))
if (length(xfactor) > 0) {
for (i in xfactor) {
if (any(! levels(xtest[[i]]) %in% xlevels[[i]]))
stop("New factor levels in xtest not present in x")
xtest[[i]] <-
factor(xlevels[[i]][match(xtest[[i]], xlevels[[i]])],
levels=xlevels[[i]])
}
}
xtest <- data.matrix(xtest)
}
} else {
ncat <- rep(1, p)
names(ncat) <- colnames(x)
xlevels <- as.list(rep(0, p))
}
maxcat <- max(ncat)
if (maxcat > 53)
stop("Can not handle categorical predictors with more than 53 categories.")
if (classRF) {
nclass <- length(levels(y))
## Check for empty classes:
if (any(table(y) == 0)) stop("Can't have empty classes in y.")
if (!is.null(ytest)) {
if (!is.factor(ytest)) stop("ytest must be a factor")
if (!all(levels(y) == levels(ytest)))
stop("y and ytest must have the same levels")
}
if (missing(cutoff)) {
cutoff <- rep(1 / nclass, nclass)
} else {
if (sum(cutoff) > 1 || sum(cutoff) < 0 || !all(cutoff > 0) ||
length(cutoff) != nclass) {
stop("Incorrect cutoff specified.")
}
if (!is.null(names(cutoff))) {
if (!all(names(cutoff) %in% levels(y))) {
stop("Wrong name(s) for cutoff")
}
cutoff <- cutoff[levels(y)]
}
}
if (!is.null(classwt)) {
if (length(classwt) != nclass)
stop("length of classwt not equal to number of classes")
## If classwt has names, match to class labels.
if (!is.null(names(classwt))) {
if (!all(names(classwt) %in% levels(y))) {
stop("Wrong name(s) for classwt")
}
classwt <- classwt[levels(y)]
}
if (any(classwt <= 0)) stop("classwt must be positive")
ipi <- 1
} else {
classwt <- rep(1, nclass)
ipi <- 0
}
} else addclass <- FALSE
if (missing(proximity)) proximity <- addclass
if (proximity) {
prox <- matrix(0.0, n, n)
proxts <- if (testdat) matrix(0, ntest, ntest + n) else double(1)
} else {
prox <- proxts <- double(1)
}
if (localImp) {
importance <- TRUE
impmat <- matrix(0, p, n)
} else impmat <- double(1)
if (importance) {
if (nPerm < 1) nPerm <- as.integer(1) else nPerm <- as.integer(nPerm)
if (classRF) {
impout <- matrix(0.0, p, nclass + 2)
impSD <- matrix(0.0, p, nclass + 1)
} else {
impout <- matrix(0.0, p, 2)
impSD <- double(p)
names(impSD) <- x.col.names
}
} else {
impout <- double(p)
impSD <- double(1)
}
nsample <- if (addclass) 2 * n else n
Stratify <- length(sampsize) > 1
if ((!Stratify) && sampsize > nrow(x)) stop("sampsize too large")
if (Stratify && (!classRF)) stop("sampsize should be of length one")
if (classRF) {
if (Stratify) {
if (missing(strata)) strata <- y
if (!is.factor(strata)) strata <- as.factor(strata)
nsum <- sum(sampsize)
if (length(sampsize) > nlevels(strata))
stop("sampsize has too many elements.")
if (any(sampsize <= 0) || nsum == 0)
stop("Bad sampsize specification")
## If sampsize has names, match to class labels.
if (!is.null(names(sampsize))) {
sampsize <- sampsize[levels(strata)]
}
if (any(sampsize > table(strata)))
stop("sampsize can not be larger than class frequency")
} else {
nsum <- sampsize
}
nrnodes <- 2 * nsum + 1
} else {
## For regression trees, need to do this to get maximal trees.
nrnodes <- 2 * sampsize + 1
}
if (!is.null(maxnodes)) {
## convert # of terminal nodes to total # of nodes
maxnodes <- 2 * maxnodes - 1
if (maxnodes > nrnodes) warning("maxnodes exceeds its max value.")
nrnodes <- min(c(nrnodes, max(c(maxnodes, 1))))
}
## Compiled code expects variables in rows and observations in columns.
x <- t(x)
storage.mode(x) <- "double"
if (testdat) {
xtest <- t(xtest)
storage.mode(xtest) <- "double"
if (is.null(ytest)) {
ytest <- labelts <- 0
} else {
labelts <- TRUE
}
} else {
xtest <- double(1)
ytest <- double(1)
ntest <- 1
labelts <- FALSE
}
nt <- if (keep.forest) ntree else 1
useweights <- 0
if (!is.null(weights)) {
if (length(weights) != n) {
stop("length of weights must match number of data points")
}
if (any(weights <= 0)) {
stop("All weights must be positive")
}
weights <- weights/sum(weights)
useweights <- 1
} else {
weights <- rep(1.0, n)
}
if (classRF) {
cwt <- classwt
threshold <- cutoff
error.test <- if (labelts) double((nclass+1) * ntree) else double(1)
rfout <- .C("classRF",
x = x,
xdim = as.integer(c(p, n)),
y = as.integer(y),
nclass = as.integer(nclass),
ncat = as.integer(ncat),
maxcat = as.integer(maxcat),
sampsize = as.integer(sampsize),
strata = if (Stratify) as.integer(strata) else integer(1),
useweights = as.integer(useweights),
weights = as.double(weights),
Options = as.integer(c(addclass,
importance,
localImp,
proximity,
oob.prox,
do.trace,
keep.forest,
replace,
Stratify,
keep.inbag)),
ntree = as.integer(ntree),
mtry = as.integer(mtry),
ipi = as.integer(ipi),
classwt = as.double(cwt),
cutoff = as.double(threshold),
nodesize = as.integer(nodesize),
outcl = integer(nsample),
counttr = integer(nclass * nsample),
prox = prox,
impout = impout,
impSD = impSD,
impmat = impmat,
nrnodes = as.integer(nrnodes),
ndbigtree = integer(ntree),
nodestatus = integer(nt * nrnodes),
bestvar = integer(nt * nrnodes),
treemap = integer(nt * 2 * nrnodes),
nodepred = integer(nt * nrnodes),
xbestsplit = double(nt * nrnodes),
errtr = double((nclass+1) * ntree),
testdat = as.integer(testdat),
xts = as.double(xtest),
clts = as.integer(ytest),
nts = as.integer(ntest),
countts = double(nclass * ntest),
outclts = as.integer(numeric(ntest)),
labelts = as.integer(labelts),
proxts = proxts,
errts = error.test,
inbag = if (keep.inbag)
matrix(integer(n * ntree), n) else integer(n),
#DUP=FALSE,
PACKAGE="randomForest")[-1]
if (keep.forest) {
## deal with the random forest outputs
max.nodes <- max(rfout$ndbigtree)
treemap <- aperm(array(rfout$treemap, dim = c(2, nrnodes, ntree)),
c(2, 1, 3))[1:max.nodes, , , drop=FALSE]
}
if (!addclass) {
## Turn the predicted class into a factor like y.
out.class <- factor(rfout$outcl, levels=1:nclass,
labels=levels(y))
names(out.class) <- x.row.names
con <- table(observed = y,
predicted = out.class)[levels(y), levels(y)]
con <- cbind(con, class.error = 1 - diag(con)/rowSums(con))
}
out.votes <- t(matrix(rfout$counttr, nclass, nsample))[1:n, ]
oob.times <- rowSums(out.votes)
if (norm.votes)
out.votes <- t(apply(out.votes, 1, function(x) x/sum(x)))
dimnames(out.votes) <- list(x.row.names, levels(y))
class(out.votes) <- c(class(out.votes), "votes")
if (testdat) {
out.class.ts <- factor(rfout$outclts, levels=1:nclass,
labels=levels(y))
names(out.class.ts) <- xts.row.names
out.votes.ts <- t(matrix(rfout$countts, nclass, ntest))
dimnames(out.votes.ts) <- list(xts.row.names, levels(y))
if (norm.votes)
out.votes.ts <- t(apply(out.votes.ts, 1,
function(x) x/sum(x)))
class(out.votes.ts) <- c(class(out.votes.ts), "votes")
if (labelts) {
testcon <- table(observed = ytest,
predicted = out.class.ts)[levels(y), levels(y)]
testcon <- cbind(testcon,
class.error = 1 - diag(testcon)/rowSums(testcon))
}
}
cl <- match.call()
cl[[1]] <- as.name("randomForest")
out <- list(call = cl,
type = if (addclass) "unsupervised" else "classification",
predicted = if (addclass) NULL else out.class,
err.rate = if (addclass) NULL else t(matrix(rfout$errtr,
nclass+1, ntree,
dimnames=list(c("OOB", levels(y)), NULL))),
confusion = if (addclass) NULL else con,
votes = out.votes,
oob.times = oob.times,
classes = levels(y),
importance = if (importance)
matrix(rfout$impout, p, nclass+2,
dimnames = list(x.col.names,
c(levels(y), "MeanDecreaseAccuracy",
"MeanDecreaseGini")))
else matrix(rfout$impout, ncol=1,
dimnames=list(x.col.names, "MeanDecreaseGini")),
importanceSD = if (importance)
matrix(rfout$impSD, p, nclass + 1,
dimnames = list(x.col.names,
c(levels(y), "MeanDecreaseAccuracy")))
else NULL,
localImportance = if (localImp)
matrix(rfout$impmat, p, n,
dimnames = list(x.col.names,x.row.names)) else NULL,
proximity = if (proximity) matrix(rfout$prox, n, n,
dimnames = list(x.row.names, x.row.names)) else NULL,
ntree = ntree,
mtry = mtry,
forest = if (!keep.forest) NULL else {
list(ndbigtree = rfout$ndbigtree,
nodestatus = matrix(rfout$nodestatus,
ncol = ntree)[1:max.nodes,, drop=FALSE],
bestvar = matrix(rfout$bestvar, ncol = ntree)[1:max.nodes,, drop=FALSE],
treemap = treemap,
nodepred = matrix(rfout$nodepred,
ncol = ntree)[1:max.nodes,, drop=FALSE],
xbestsplit = matrix(rfout$xbestsplit,
ncol = ntree)[1:max.nodes,, drop=FALSE],
pid = rfout$classwt, cutoff=cutoff, ncat=ncat,
maxcat = maxcat,
nrnodes = max.nodes, ntree = ntree,
nclass = nclass, xlevels=xlevels)
},
y = if (addclass) NULL else y,
test = if(!testdat) NULL else list(
predicted = out.class.ts,
err.rate = if (labelts) t(matrix(rfout$errts, nclass+1,
ntree,
dimnames=list(c("Test", levels(y)), NULL))) else NULL,
confusion = if (labelts) testcon else NULL,
votes = out.votes.ts,
proximity = if(proximity) matrix(rfout$proxts, nrow=ntest,
dimnames = list(xts.row.names, c(xts.row.names,
x.row.names))) else NULL),
inbag = if (keep.inbag) matrix(rfout$inbag, nrow=nrow(rfout$inbag),
dimnames=list(x.row.names, NULL)) else NULL)
} else {
ymean <- mean(y)
y <- y - ymean
ytest <- ytest - ymean
rfout <- .C("regRF",
x,
as.double(y),
as.integer(useweights),
as.double(weights),
as.integer(c(n, p)),
as.integer(sampsize),
as.integer(nodesize),
as.integer(nrnodes),
as.integer(ntree),
as.integer(mtry),
as.integer(c(importance, localImp, nPerm)),
as.integer(ncat),
as.integer(maxcat),
as.integer(do.trace),
as.integer(proximity),
as.integer(oob.prox),
as.integer(corr.bias),
ypred = double(n),
impout = impout,
impmat = impmat,
impSD = impSD,
prox = prox,
ndbigtree = integer(ntree),
nodestatus = matrix(integer(nrnodes * nt), ncol=nt),
leftDaughter = matrix(integer(nrnodes * nt), ncol=nt),
rightDaughter = matrix(integer(nrnodes * nt), ncol=nt),
nodepred = matrix(double(nrnodes * nt), ncol=nt),
bestvar = matrix(integer(nrnodes * nt), ncol=nt),
xbestsplit = matrix(double(nrnodes * nt), ncol=nt),
mse = double(ntree),
keep = as.integer(c(keep.forest, keep.inbag)),
replace = as.integer(replace),
testdat = as.integer(testdat),
xts = xtest,
ntest = as.integer(ntest),
yts = as.double(ytest),
labelts = as.integer(labelts),
ytestpred = double(ntest),
proxts = proxts,
msets = double(if (labelts) ntree else 1),
coef = double(2),
oob.times = integer(n),
inbag = if (keep.inbag)
matrix(integer(n * ntree), n) else integer(1),
#DUP=FALSE,
PACKAGE="randomForest")[c(18:30, 38:43)]
## Format the forest component, if present.
if (keep.forest) {
max.nodes <- max(rfout$ndbigtree)
rfout$nodestatus <-
rfout$nodestatus[1:max.nodes, , drop=FALSE]
rfout$bestvar <-
rfout$bestvar[1:max.nodes, , drop=FALSE]
rfout$nodepred <-
rfout$nodepred[1:max.nodes, , drop=FALSE] + ymean
rfout$xbestsplit <-
rfout$xbestsplit[1:max.nodes, , drop=FALSE]
rfout$leftDaughter <-
rfout$leftDaughter[1:max.nodes, , drop=FALSE]
rfout$rightDaughter <-
rfout$rightDaughter[1:max.nodes, , drop=FALSE]
}
cl <- match.call()
cl[[1]] <- as.name("randomForest")
## Make sure those obs. that have not been OOB get NA as prediction.
ypred <- rfout$ypred
if (any(rfout$oob.times < 1)) {
ypred[rfout$oob.times == 0] <- NA
}
out <- list(call = cl,
type = "regression",
predicted = structure(ypred + ymean, names=x.row.names),
mse = rfout$mse,
rsq = 1 - rfout$mse / (var(y) * (n-1) / n),
oob.times = rfout$oob.times,
importance = if (importance) matrix(rfout$impout, p, 2,
dimnames=list(x.col.names,
c("%IncMSE","IncNodePurity"))) else
matrix(rfout$impout, ncol=1,
dimnames=list(x.col.names, "IncNodePurity")),
importanceSD=if (importance) rfout$impSD else NULL,
localImportance = if (localImp)
matrix(rfout$impmat, p, n, dimnames=list(x.col.names,
x.row.names)) else NULL,
proximity = if (proximity) matrix(rfout$prox, n, n,
dimnames = list(x.row.names, x.row.names)) else NULL,
ntree = ntree,
mtry = mtry,
forest = if (keep.forest)
c(rfout[c("ndbigtree", "nodestatus", "leftDaughter",
"rightDaughter", "nodepred", "bestvar",
"xbestsplit")],
list(ncat = ncat), list(nrnodes=max.nodes),
list(ntree=ntree), list(xlevels=xlevels)) else NULL,
coefs = if (corr.bias) rfout$coef else NULL,
y = y + ymean,
test = if(testdat) {
list(predicted = structure(rfout$ytestpred + ymean,
names=xts.row.names),
mse = if(labelts) rfout$msets else NULL,
rsq = if(labelts) 1 - rfout$msets /
(var(ytest) * (n-1) / n) else NULL,
proximity = if (proximity)
matrix(rfout$proxts / ntree, nrow = ntest,
dimnames = list(xts.row.names,
c(xts.row.names,
x.row.names))) else NULL)
} else NULL,
inbag = if (keep.inbag)
matrix(rfout$inbag, nrow(rfout$inbag),
dimnames=list(x.row.names, NULL)) else NULL)
}
class(out) <- "randomForest"
return(out)
}
|
