gbmCMA {CMA} | R Documentation

## Tree-based Gradient Boosting

### Description

Roughly speaking, Boosting combines 'weak learners' in a weighted manner in a stronger ensemble. This method calls the function `gbm.fit` from the package `gbm`. The 'weak learners' are simple trees that need only very few splits (default: 1).

For `S4` method information, see `gbmCMA-methods`.

### Usage

    gbmCMA(X, y, f, learnind, ...)

### 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.

`...`

Further arguments passed to the function `gbm.fit` from the package of the same name. Worth mentionning are

`ntrees`
  Number of trees to fit (size of the ensemble), defaults to 100. This parameter should be optimized using `tune`.
`shrinkage`
  The learning rate (default is 0.001). Usually fixed to a very low value.
`distribution`
  Loss function to be used. Default is `"bernoulli"`, i.e. `LogitBoost`, a (less robust) alternative is `"adaboost"`.
`interaction.depth`
  Number of splits used by the 'weak learner' (single decision tree). Default is `1`.

### Value

An onject of class `cloutput`.

### Note

Up to now, this method can only be applied to binary classification.

### Author(s)

Martin Slawski martin.slawski@campus.lmu.de

Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix

### References

Ridgeway, G. (1999).

The state of boosting.

Computing Science and Statistics, 31:172-181

Friedman, J. (2001).

Greedy Function Approximation: A Gradient Boosting Machine.

Annals of Statistics 29(5):1189-1232.

### See Also

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

### Examples

    ### load Golub AML/ALL data
    data(golub)
    ### extract class labels
    golubY <- golub[,1]
    ### extract gene expression
    golubX <- as.matrix(golub[,-1])
    ### select learningset
    ratio <- 2/3
    set.seed(111)
    learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
    ### run tree-based gradient boosting (no tuning)
    gbmresult <- gbmCMA(X=golubX, y=golubY, learnind=learnind, n.trees = 500)
    show(gbmresult)
    ftable(gbmresult)
    plot(gbmresult)

[Package CMA version 1.2.0 Index]