compBoostCMA: Componentwise Boosting
In chbernau/CMA: Synthesis of microarray-based classification

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

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

`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.

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'.

An object of class clvarseloutput.

Martin Slawski ms@cs.uni-sb.de

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

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.

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

 ### 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)