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, ...)
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. |
delta |
The shrinkage intensity for the class centroids -
a hyperparameter that must be tuned. The default
0.5 not necessarily produces good results. |
... |
Currently unused argument. |
An object of class cloutput
.
The results can differ from those obtained by
using the package pamr
.
Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
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)