scdaCMA {CMA} | R Documentation |
The nearest shrunken centroid classification algorithm is detailly described in Tibshirani et al. (2002).
It is widely known under the name PAM (prediction analysis for microarrays),
which can also be found in the package pamr
.
For S4
method information, see scdaCMA-methods.
scdaCMA(X, y, f, learnind, delta = 0.5, 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 |
delta |
The shrinkage intensity for the class centroids -
a hyperparameter that must be tuned. The default
|
models |
a logical value indicating whether the model object shall be returned |
... |
Currently unused argument. |
An object of class cloutput
.
The results can differ from those obtained by
using the package pamr
.
Martin Slawski ms@cs.uni-sb.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
Tibshirani, R., Hastie, T., Narasimhan, B., and Chu, G., (2003).
Class prediction by nearest shrunken centroids with applications to DNA microarrays.
Statistical Science, 18, 104-117
compBoostCMA
, dldaCMA
, ElasticNetCMA
,
fdaCMA
, flexdaCMA
, gbmCMA
,
knnCMA
, ldaCMA
, LassoCMA
,
nnetCMA
, pknnCMA
, plrCMA
,
pls_ldaCMA
, pls_lrCMA
, pls_rfCMA
,
pnnCMA
, qdaCMA
, rfCMA
,
shrinkldaCMA
, svmCMA
### 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(2/3*length(khanY))) ### run Shrunken Centroids classfier, without tuning scdaresult <- scdaCMA(X=khanX, y=khanY, learnind=learnind) ### show results show(scdaresult) ftable(scdaresult) plot(scdaresult)