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, ...)
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 tune d. |
scale |
Scale the predictors as specified by X to have unit variance
and zero mean. |
... |
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 martin.slawski@campus.lmu.de,
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix.
Zhu, J., Hastie, T. (2004). Classification of gene microarrays by penalized logistic regression.
Biostatistics 5:427-443.
code{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)