IVCT_Interval {IVCor} | R Documentation |
Integrated Variance Correlation Based Interval Independence Hypothesis Test
Description
This function is used to test interval independence using integrated variance correlation
Usage
IVCT_Interval(y, x, tau1, tau2, K, num_per, NN = 3, type)
Arguments
y |
is the response vector |
x |
is a numeric vector or a data matrix |
tau1 |
is the minimum quantile level |
tau2 |
is the maximum quantile level |
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 |
Value
The p-value of the corresponding hypothesis test
Examples
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=rnorm(n)
IVCT_Interval(y,x,tau1=0.5,tau2=0.75,K=5,num_per=20,type = "linear")
n=100
x_til=runif(n,min=-1,max=1)
y_til=rnorm(n)
epsilon=rnorm(n)
x=x_til+2*epsilon*ifelse(x_til<=-0.5&y_til<=-0.675,1,0)
y=y_til+2*epsilon*ifelse(x_til<=-0.5&y_til<=-0.675,1,0)
IVCT_Interval(y,x,tau1=0.6,tau2=0.8,K=5,num_per=20,type = "nonlinear")
[Package IVCor version 0.1.0 Index]