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, 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. Must not be missing for this method. |
models |
a logical value indicating whether the model object shall be returned |
... |
Further arguments to be passed to |
An object of class cloutput
.
Class probabilities are not returned. For a probabilistic
variant of knn
, s. pknnCMA
.
Martin Slawski ms@cs.uni-sb.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
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)