Description Usage Arguments Value Author(s) References See Also Examples
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.
1 | 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, ??, ???-???.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | # 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
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.