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:
WARNING: The class labels will be re-coded to
range from |
f |
A two-sided formula, if |
learnind |
An index vector specifying the observations that
belong to the learning set. May be |
lambda |
Parameter governing the amount of penalization.
This hyperparameter should be |
scale |
Scale the predictors as specified by |
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)