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# --- Functions Called by Main Function Run_Perm
"iJRF_Perm" <-
function(X, Y, W,ntree=NULL,mtry=NULL,res.name=NULL,cov.name=NULL,P) {
# --- X[[j]] (M x n) matrix containing predictors for class j
# --- Y[[j]] (p x n) matrix containing response for class j
# --- W (M x p) matrix of scores
p<-dim(Y[[1]])[1]; M<-dim(X[[1]])[1];
if (is.null(mtry)) mtry=sqrt(M)
if (is.null(ntree)) ntree=1000
if (is.null(res.name)) res.name=paste("G",seq(1,p),sep="")
if (is.null(cov.name)) cov.name=paste("M",seq(1,M),sep="")
nclasses<-length(X)
sampsize<-rep(0,nclasses)
imp<-array(0,c(M,p,nclasses))
imp.final<-array(0,c(p*M,P,nclasses));
for (j in 1:nclasses) { X[[j]] <- t(apply(X[[j]], 1, function(x) { (x - mean(x)) / sd(x) } ))
sampsize[j]<-dim(X[[j]])[2]
Y[[j]] <- t(apply(Y[[j]], 1, function(x) { (x - mean(x)) / sd(x) } )) }
tot<-max(sampsize);
for (perm in 1:P){
for (j in 1:length(res.name)){
set.seed((perm-1)*nclasses+1)
weights.rf<-as.matrix(W[,j]);
weights.rf<-weights.rf/sum(weights.rf);
w.sorted<-sort(weights.rf,decreasing = FALSE,index.return=T)
index<-w.sorted$ix
w.sorted<-w.sorted$x
covar<-matrix(0,M*nclasses,tot)
y<-matrix(0,nclasses,tot)
for (c in 1:nclasses) {
y[c,seq(1,sampsize[c])]<-Y[[c]][j,sample(sampsize[c])]
covar[seq((c-1)*(M)+1,c*M),seq(1,sampsize[c])]<-X[[c]][index,]
}
jrf.out<-iJRF_onetarget(x=covar,y=y,mtry=mtry,importance=TRUE,sampsize=sampsize,
nclasses=nclasses,ntree=ntree,sw=as.double(w.sorted))
for (s in 1:nclasses) imp[index,j,s]<-importance(jrf.out,scale=FALSE)[seq(M*(s-1)+1,M*s)] #- save importance score for net1
}
for (s in 1:nclasses) imp.final[,perm,s]<-c(imp[,,s])
}
return(imp.final)
}
"iJRF_onetarget" <-
function(x, y=NULL, xtest=NULL, ytest=NULL, ntree,
sampsize,
totsize = if (replace) ncol(x) else ceiling(.632*ncol(x)),
mtry=if (!is.null(y) && !is.factor(y))
max(floor(nrow(x)/3), 1) else floor(sqrt(nrow(x))),
replace=TRUE, classwt=NULL, cutoff, strata,
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, nclasses, sw,...) {
ww=1/sampsize;
nclass=mylevels=ipi=NULL
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 <- ncol(y) # number of samples
p <- nrow(x)/nclasses # number of variables
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)
keep.forest=!is.null(y)
xtest=NULL; ytest=NULL
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)
}
prox <- proxts <- double(1)
## 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)
xlevels <- as.list(rep(0, p))
}
maxcat <- max(ncat)
if (maxcat > 32)
stop("Can not handle categorical predictors with more than 32 categories.")
addclass <- FALSE
proximity <- addclass
impout <- matrix(0.0, p*nclasses, 2)
impSD <- matrix(0.0, p*nclasses, 1)
# names(impSD) <- x.col.names
nsample <- if (addclass) 2 * n else n
Stratify <- length(n) > 1
nodesize=5;
nrnodes <- 2 * trunc(n/max(1, nodesize - 4)) + 1
maxnodes=NULL
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"
xtest <- double(1)
ytest <- double(1)
ntest <- 1
labelts <- FALSE
nt <- if (keep.forest) ntree else 1
nPerm=1
do.trace=F; oob.prox=F
corr.bias=FALSE
keep.inbag=FALSE
impmat <- double(1)
replace=T
rfout <- .C("iJRF_regRF",
x,
y, ww,
as.integer(c(totsize, p)),
sampsize=as.integer(sampsize), as.integer(totsize),
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 * nclasses),
impout = impout,
impmat = impmat,
impSD = impSD,
prox = prox,
ndbigtree = integer(ntree),
nodestatus = matrix(integer(nrnodes * nt * nclasses), ncol=nt),
leftDaughter = matrix(integer(nrnodes * nt * nclasses), ncol=nt),
rightDaughter = matrix(integer(nrnodes * nt * nclasses), ncol=nt),
nodepred = matrix(double(nrnodes * nt * nclasses), ncol=nt),
bestvar = matrix(integer(nrnodes * nt * nclasses), ncol=nt),
xbestsplit = matrix(double(nrnodes * nt * nclasses), ncol=nt),
mse = double(ntree * nclasses),
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), as.integer(nclasses),
sw = as.double(sw))[c(16:28, 36:41)]
# ## 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$nodepred[1:max.nodes, , drop=FALSE]
rfout$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 =0,
mse = rfout$mse,
rsq = 1 - rfout$mse / (var(y[1,]) * (n-1) / n),
oob.times = rfout$oob.times,
importance = if (importance) matrix(rfout$impout, p * nclasses, 2) else
matrix(rfout$impout, ncol=1),
importanceSD=if (importance) rfout$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$coef else NULL,
y = y,
test = if(testdat) {
list(predicted = structure(rfout$ytestpred,
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$inbag),
dimnames=list(x.row.names, NULL)) else NULL)
# print(rfout$mse)
class(out) <- "randomForest"
return(out)
}
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