skda {skda} | R Documentation |
This function implements the sparse (multicategory) kernel discriminant analysis with function
skda
with cvskda
to tune regularization parameter via cross validation. The other function
predprob
predicts the conditional class probability.
skda(x,y, tau, method="Bayes")
x |
a matrix ( n X p ) that contains predictors. |
y |
a vector that contains the categorical response coded as 1, 2, ..., K. |
tau |
a positive number that is the regularization parameter. |
method |
method (mle or Bayes) to be used in the KDA classifier. |
lam |
the SKDA solution of size p X 1. |
phat |
the predicted conditional class probabilities of size n X K. |
L. A. Stefanski, Y. Wu, and K. White
L. A. Stefanski, Y. Wu, and K. White (2013) Variable selection in nonparametric classification via measurement error model selection likelihoods Journal of the American Statistical Association, ??, ???-???.
# a binary example rm(list=ls()) n=200 p=20 r=0.8 x=matrix(rnorm(n*p,mean=0,sd=1),n,p) y=rbinom(n,1,0.5) x[,3]=x[,3]+(2*y-1)*r x[,11]=x[,11]+(2*y-1)*r y=y+1 ind1=which(y>1.5) ind0=which(y<1.5) plot(-4:4, -4:4, type = "n") points(x[ind1, 3], x[ind1,11],col="blue") points(x[ind0, 3], x[ind0,11],col="red") lam=skda(x,y,3)$lam # a three-class example rm(list=ls()) n=200 p=20 r=2 x=matrix(rnorm(n*p,mean=0,sd=1),n,p) y=ceiling(runif(n,0,3)) thetas=c(0, 2*pi/3, 4*pi/3) x[,3]=x[,3]+r*cos(thetas[y]) x[,11]=x[,11]+r*sin(thetas[y]) ind1=which(y==1) ind2=which(y==2) ind3=which(y==3) plot(-6:6, -6:6, type = "n") points(x[ind1, 3], x[ind1,11],col="blue") points(x[ind2, 3], x[ind2,11],col="red") points(x[ind3, 3], x[ind3,11],col="black") lam=skda(x,y,3)$lam