knnCMA {CMA} | R Documentation |
Ordinary k
nearest neighbours algorithm from the
very fast implementation in the package class
.
For S4
method information, see knnCMA-methods.
knnCMA(X, y, f, learnind, ...)
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. Must not be missing for this method. |
... |
Further arguments to be passed to knn from the
package class , in particular the number of
nearest neighbours to use (argument k ). |
An object of class cloutput
.
Class probabilities are not returned. For a probabilistic
variant of knn
, s. pknnCMA
.
Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
Ripley, B.D. (1996)
Pattern Recognition and Neural Networks.
Cambridge University Press
compBoostCMA
, dldaCMA
, ElasticNetCMA
,
fdaCMA
, flexdaCMA
, gbmCMA
,
ldaCMA
, LassoCMA
,
nnetCMA
, pknnCMA
, plrCMA
,
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 k-nearest neighbours result <- knnCMA(X=golubX, y=golubY, learnind=learnind, k = 3) ### show results show(result) ftable(result) ### multiclass example: ### 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(ratio*length(khanY))) ### run knn result <- knnCMA(X=khanX, y=khanY, learnind=learnind, k = 5) ### show results show(result) ftable(result)