IVCor (version 0.1.0)

IVCT: Integrated Variance Correlation Based Hypothesis Test

Description

This function is used to test significance of linear or nonlinear correlation using integrated variance correlation

Usage

IVCT(y, x, K, num_per, NN = 3, type)

Value

The p-value of the corresponding hypothesis test

Arguments

y

is the response vector

x

is a numeric vector or a data matrix

K

is the number of quantile levels

num_per

is the number of permutation times

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

# linear model
n=100
x=rnorm(n)
y=rnorm(n)

IVCT(y,x,K=5,num_per=20,type = "linear")
# nonlinear model
n=100
p=4
x=matrix(NA,nrow=n,ncol=p)
for(i in 1:p){
 x[,i]=runif(n,0,1)
}
y=3*ifelse(x[,1]>0.5,1,0)*x[,2]+3*cos(x[,3])^2*x[,1]+3*(x[,4]^2-1)*x[,1]+rnorm(n)

IVCT(y,x,K=5,num_per=20,type = "nonlinear")