Usage
rfCMA(X, y, f, learnind, varimp = TRUE, seed = 111, models=FALSE,type=1,scale=FALSE,importance=TRUE, ...)
Arguments
X
Gene expression data. Can be one of the following:
- A
matrix. Rows correspond to observations, columns to variables.
- A
data.frame, when f is not missing (s. below).
- An object of class
ExpressionSet.
y
Class labels. Can be one of the following:
- A
numeric vector.
- A
factor.
- A
character if X is an ExpressionSet that
specifies the phenotype variable.
-
missing, if X is a data.frame and a
proper formula f is provided.
WARNING: The class labels will be re-coded to
range from 0 to K-1, where K is the
total number of different classes in the learning set.
f
A two-sided formula, if X is a data.frame. The
left part correspond to class labels, the right to variables.
learnind
An index vector specifying the observations that
belong to the learning set. May be missing;
in that case, the learning set consists of all
observations and predictions are made on the
learning set.
varimp
Should additional information for variable selection be provided ? Defaults to TRUE.
seed
Fix Random number generator seed to seed. This is
useful to guarantee reproducibility of the results.
models
a logical value indicating whether the model object shall be returned
type
Parameter passed to function importance. Either 1 or 2, specifying the type of importance measure
(1=mean decrease in accuracy, 2=mean decrease in node
impurity).
scale
Parameter passed to function importance. For permutation based measures, should the measures be
divided by their standard errors?
importance
Parameter passed to function randomForest.Should importance of predictors be assessed by permutation?
...
Further arguments to be passed to randomForest from the
package of the same name.