| plrCMA {CMA} | R Documentation |
High dimensional logistic regression combined with an
L2-type (Ridge-)penalty.
Multiclass case is also possible.
For S4 method information, see plrCMA-methods
plrCMA(X, y, f, learnind, lambda = 0.01, scale = TRUE, models=FALSE,...)
X |
Gene expression data. Can be one of the following:
|
y |
Class labels. Can be one of the following:
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 tuned. |
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)