wsrf ModelGive the predictions for the new data by the forest
model built from wsrf.
# S3 method for wsrf
predict(object, newdata, type=c("response",
"class", "vote", "prob", "aprob", "waprob"), ...)a list of predictions for the new data with corresponding components for
each type of predictions. For type=class or type=class, a
vector of length nrow(newdata), otherwise, a matrix of
nrow(newdata) * (number of class label). For example, if given
type=c("class", "prob") and the return value is res, then
res$class is a vector of predicted class labels of length
nrow(newdata), and res$prob is a matrix of class probabilities.
object of class wsrf.
the data that needs to be predicted. Its format
should be the same as that for wsrf.
the type of prediction required, a character vector indicating the types of output, and can be one of the values below:
matrix of vote counts
predicted values.
the same as response.
matrix of class probabilities. The probability is the proportion of trees in the forest voting for the particular outcome (prob = votes / ntree)
the average score from the decision trees for each class rather than the proportion of decision trees for each class (aprob = scores / ntree)
the weighted average, weighted by the accuracy of the tree (waprob = scores * accuracy / sum(accuracy))
optional additional arguments. At present no additional arguments are used.
He Zhao and Graham Williams (SIAT, CAS)
wsrf