CMA (version 1.30.0)

compBoostCMA: Componentwise Boosting

Description

Roughly speaking, Boosting combines 'weak learners' in a weighted manner in a stronger ensemble.

'Weak learners' here consist of linear functions in one component (variable), as proposed by Buehlmann and Yu (2003).

It also generates sparsity and can as well be as used for variable selection alone. (s. GeneSelection).

For S4 method information, see compBoostCMA-methods.

Usage

compBoostCMA(X, y, f, learnind, loss = c("binomial", "exp", "quadratic"), mstop = 100, nu = 0.1, models=FALSE, ...)

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.

loss

Character specifying the loss function - one of "binomial" (LogitBoost), "exp" (AdaBoost), "quadratic"(L2Boost).

mstop

Number of boosting iterations, i.e. number of updates to perform. The default (100) does not necessarily produce good results, therefore usage of tune for this argument is highly recommended.

nu

Shrinkage factor applied to the update steps, defaults to 0.1. In most cases, it suffices to set nu to a very low value and to concentrate on the optimization of mstop.

models

a logical value indicating whether the model object shall be returned

...

Currently unused arguments.

Value

clvarseloutput.

Details

The method is partly based on code from the package mboost from T. Hothorn and P. Buehlmann.

The algorithm for the multiclass case is described in Lutz and Buehlmann (2006) as 'rowwise updating'.

References

Buelmann, P., Yu, B. (2003).

Boosting with the L2 loss: Regression and Classification.

Journal of the American Statistical Association, 98, 324-339

Buehlmann, P., Hothorn, T.

Boosting: A statistical perspective.

Statistical Science (to appear) Lutz, R., Buehlmann, P. (2006).

Boosting for high-multivariate responses in high-dimensional linear regression.

Statistica Sinica 16, 471-494.

See Also

dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, 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 componentwise (logit)-boosting (not tuned)
result <- compBoostCMA(X=golubX, y=golubY, learnind=learnind, mstop = 500)
### show results
show(result)
ftable(result)
plot(result)
### multiclass example:
### 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(ratio*length(khanY)))
### run componentwise multivariate (logit)-boosting (not tuned)
result <- compBoostCMA(X=khanX, y=khanY, learnind=learnind, mstop = 1000)
### show results
show(result)
ftable(result)
plot(result)