IVCor (version 0.1.0)

IVC_Interval: Integrated Variance Correlation for Interval Independence

Description

This function is used to calculate the integrated variance correlation to measure interval independence

Usage

IVC_Interval(y, x, K, tau1, tau2, NN = 3, type)

Value

The value of the corresponding sample statistic for interval independence

Arguments

y

is a numeric vector

x

is a numeric vector or a data matrix

K

is the number of quantile levels

tau1

is the minimum quantile level

tau2

is the maximum quantile level

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
require("mvtnorm")
n=100
p=3
pho1=0.5
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")
y=2*(x[,1]+x[,2]+x[,3])+rnorm(n)

IVC_Interval(y,x,K=5,tau1=0.4,tau2=0.6,type="linear")
# nonlinear model
n=100
x=runif(n,min=-2,max=2)
y=exp(x^2)*rnorm(n)

IVC_Interval(y,x,K=5,tau1=0.4,tau2=0.6,type="nonlinear")