rfCMA {CMA} | R Documentation

## Classification based on Random Forests

### Description

Random Forests were proposed by Breiman (2001) and are implemented in the package `randomForest`.

In this package, they can as well be used to rank variables according to their importance, s. `GeneSelection`.

For `S4` method information, see rfCMA-methods

### 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.

### Value

If `varimp`, then an object of class `clvarseloutput` is returned, otherwise an object of class `cloutput`

### Author(s)

Martin Slawski ms@cs.uni-sb.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

### References

Breiman, L. (2001)

Random Forest.

Machine Learning, 45:5-32.

### See Also

`compBoostCMA`, `dldaCMA`, `ElasticNetCMA`, `fdaCMA`, `flexdaCMA`, `gbmCMA`, `knnCMA`, `ldaCMA`, `LassoCMA`, `nnetCMA`, `pknnCMA`, `plrCMA`, `pls_ldaCMA`, `pls_lrCMA`, `pls_rfCMA`, `pnnCMA`, `qdaCMA`, `scdaCMA`, `shrinkldaCMA`, `svmCMA`

### Examples

     ### load Khan data
    data(khan)
    ### extract class labels
    khanY <- khan[,1]
    ### extract gene expression
    khanX <- as.matrix(khan[,-1])
    ### select learningset
    set.seed(111)
    learnind <- sample(length(khanY), size=floor(2/3*length(khanY)))
    ### run random Forest
    #rfresult <- rfCMA(X=khanX, y=khanY, learnind=learnind, varimp = FALSE)
    ### show results
    #show(rfresult)
    #ftable(rfresult)
    #plot(rfresult)

[Package CMA version 1.44.0 Index]