Usage
prandomForest(x, ...)
"prandomForest"(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))), 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, ...)
Arguments
...
optional parameters to be passed to the low level function
randomForest.default.
y
vector, if a factor, classification is assumed, otherwise
regression is assumed. If omitted, prandomForest() will run
in unsupervised mode.
xtest
data array of predictors for the test set
ytest
response for the test set
ntree
integer, the number of trees to grow
mtry
integer, the number of variables randomly sampled as
candidates at each split. The default value is sqrt(p) for
classification and p/3 for regression, where p is the
number of variables in the data matrix x.
replace
boolean, whether the sampling of cases is done with or
without replacement. The default value is TRUE.
classwt
vector if priors of the classes. The default value is
NULL.
cutoff
vector of k elements where k is the number of classes.
The winning class for an observation is the one with the
maximum ratio of proportion of votes to cutoff. The
default value is 1/k.
strata
variable used for stratified sampling
sampsize
size of sample to draw. For classification, if
sampsize is a vector of the length of the number of
strata, then sampling is stratified by strata, and the
elements of sampsize indicate the numbers to be drawn
from the strata.
nodesize
integer, the minimum size of the terminal nodes. The
default value is 1 for classification and 5 for
regression.
maxnodes
integer, maximum number of terminal nodes allowed for
the trees. The default value is NULL.
importance
boolean, whether the importance of predictors is
assessed. The default value is FALSE.
localImp
boolean, whether casewise importance measure is to be
computed. The default value is FALSE.
nPerm
integer, the number of times the out-of-bag data are
permuted per tree for assessing variable importance. The
default value is one. Regression only.
proximity
boolean, whether the proximity measure among the rows
is to be calculated.
oob.prox
boolean, whether the proximity is to be calculated for
out-of-bag data. The default value is set to be the
same as the value of the proximity parameter.
norm.votes
boolean, whether the final result of votes are
expressed as fractions or whether the raw vote
counts are returned. The default value is TRUE.
Classification only.
do.trace
boolean, whether a verbose output is produced. The
default value is FALSE. If set to an integer i then
the output is printed for every i trees.
keep.forest
boolean, whether the forest is returned in the
output object. The default value is FALSE.
corr.bias
boolean, whether to perform a bias correction. The
default value is FALSE. Regression only.
keep.inbag
boolean, whether the matrix which keeps track of
which samples are in-bag in which trees should be
returned. The default value is FALSE.