IVCCAT {IVCor} | R Documentation |
Integrated Variance Correlation Based Hypothesis Test for Discrete Response
Description
This function is used to test independence between a categorical variable and a continuous variable using integrated variance correlation
Usage
IVCCAT(y, x, K, num_per, type)
Arguments
y |
is a categorical response vector |
x |
is a numeric vector |
K |
is the number of quantile levels |
num_per |
is the number of permutation times |
type |
is an indicator for fixed number of categories or infinity number of categories, "fixed" represents number of categories is fixed, then a permutation test is used, "infinity" represents number of categories is infinite, then an asymptotic normal distribution is used to calculate p values |
Value
The p-value of the corresponding hypothesis test
Examples
# small R
n=100
x=runif(n,0,1)
y=sample(rep(1:3), n, replace = TRUE, prob = c(1/3,1/3,1/3))
IVCCAT(y,x,K=5,num_per=20,type = "fixed")
# large R
n=200
y=sample(rep(1:20), n, replace = TRUE, prob = rep(1/20,20))
mu_x=sample(c(1,2,3,4),20,replace = TRUE,prob = c(1/4,1/4,1/4,1/4))
x=c()
for (i in 1:n) {
x[i]=2*mu_x[y[i]]+rcauchy(1)
}
IVCCAT(y,x,K=10,type = "infinity")
[Package IVCor version 0.1.0 Index]