X2kernel {plsdof} | R Documentation |
This function computes the Gram matrices for a gaussian kernel and differnt kernel widths.
X2kernel(X, Xtest = NULL, sigma = 1)
X |
matrix of predictor observations. |
Xtest |
optional matrix of test observations. Default is Xtest=NULL .
|
sigma |
vector of kernel widths. Default is sigma=1 .
|
We first scale X
such that the range of each column lies in [-1,1]. Additionally, the data is centered in feature space.
If test data is provided, the same transformations are also applied to them.
The default value for sigma
is in general NOT a sensible parameter, and sigma
should always be selected via a model selection criterion.
K |
array of kernel matrices |
Ktest |
array of kernel matrices for Xtest , if provided |
sigma |
vector of kernel widths |
Nicole Kraemer
X2kernel
, kernel.pls
, kernel.pls.ic
, kernel.pls.cv
p<-20 # number of variables n<-100 # number of observations ntest<-50 # number of test observations X<-matrix(rnorm(n*p),ncol=p) Xtest<-matrix(rnorm(ntest*p),ncol=p) sigma<-exp(seq(0,4,length=10)) kernel.object<-X2kernel(X,Xtest,sigma=sigma)