plrCMA: L2 penalized logistic regression
In CMA: Synthesis of microarray-based classification

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

High dimensional logistic regression combined with an L2-type (Ridge-)penalty. Multiclass case is also possible. For S4 method information, see plrCMA-methods

1	plrCMA(X, y, f, learnind, lambda = 0.01, scale = TRUE, 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.
`lambda`	Parameter governing the amount of penalization. This hyperparameter should be `tune`d.
`scale`	Scale the predictors as specified by `X` to have unit variance and zero mean.
`models`	a logical value indicating whether the model object shall be returned
`...`	Currently unused argument.

An object of class cloutput.

Special thanks go to

Ji Zhu (University of Ann Arbor, Michigan)

Trevor Hastie (Stanford University)

who provided the basic code that was then adapted by

Martin Slawski ms@cs.uni-sb.de

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

Zhu, J., Hastie, T. (2004). Classification of gene microarrays by penalized logistic regression.

Biostatistics 5:427-443.

compBoostCMA, dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, ldaCMA, LassoCMA, nnetCMA, pknnCMA, 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 from first 10 genes
golubX <- as.matrix(golub[,-1])
### select learningset
ratio <- 2/3
set.seed(111)
learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
### run penalized logistic regression (no tuning)
plrresult <- plrCMA(X=golubX, y=golubY, learnind=learnind)
### show results
show(plrresult)
ftable(plrresult)
plot(plrresult)
### multiclass example:
### load Khan data
data(khan)
### extract class labels
khanY <- khan[,1]
### extract gene expression from first 10 genes
khanX <- as.matrix(khan[,-1])
### select learningset
set.seed(111)
learnind <- sample(length(khanY), size=floor(ratio*length(khanY)))
### run penalized logistic regression (no tuning)
plrresult <- plrCMA(X=khanX, y=khanY, learnind=learnind)
### show results
show(plrresult)
ftable(plrresult)
plot(plrresult)

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

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binary Classification with penalized logistic regression
number of predictions: 13
number of missclassifications:  0 
missclassification rate:  0 
sensitivity: 1 
specificity: 1 
    predicted
true 0 1
   0 8 0
   1 0 5

multiclass Classification with penalized logistic regression
number of predictions: 21
number of missclassifications:  0 
missclassification rate:  0 
    predicted
true 0 1 2 3
   0 2 0 0 0
   1 0 9 0 0
   2 0 0 5 0
   3 0 0 0 5