newIMVC (version 0.1.0)

IMVCS: Integrated Mean Variance Correlation Based Screening

Description

This function is used to select important features using integrated mean variance correlation

Usage

IMVCS(y, x, K, d, NN = 3, type)

Value

The labels of first d largest active set of all predictors

Arguments

y

is the response vector

x

is the covariate matrix

K

is the number of quantile levels

d

is the size of selected variables

NN

is the number of B spline basis, default is 3

type

is an indicator for measuring linear or nonlinear correlation, "linear" represents linear correlation and "nonlinear" represents linear or nonlinear correlation using B splines

Examples

require("mvtnorm")
n=200
p=500
pho1=0.8
mean_x=rep(0,p)
sigma_x=matrix(NA,nrow = p,ncol = p)
for (i in 1:p) {
 for (j in 1:p) {
   sigma_x[i,j]=pho1^(abs(i-j))
 }
}
x=rmvnorm(n, mean = mean_x, sigma = sigma_x,method = "chol")
x1=x[,1]
x2=x[,2]
x3=x[,12]
x4=x[,22]
y=2*x1+0.5*x2+3*x3*ifelse(x3<0,1,0)+2*x4+rnorm(n)

IMVCS(y,x,K=5,d=round(n/log(n)),type="nonlinear")