meanConsTest {MCARtest} | R Documentation |
Carry out a test of MCAR checking consistency of mean vectors.
Description
This is the implementation of Algorithm 2 in Bordino and Berrett (2025).
Usage
meanConsTest(X, B)
Arguments
X |
The dataset with incomplete data. |
B |
The bootstrap sample |
Value
The p-value of the test of MCAR based on mean vectors, as outlined in Algorithm 2 in Bordino and Berrett (2025).
References
Bordino A, Berrett TB (2025). “Tests of Missing Completely At Random based on sample covariance matrices.” Ann. Statist., to appear, arXiv:2401.05256.
Examples
library(MASS)
alpha = 0.05
B = 20
m = 500
SigmaS=list() #Random 2x2 correlation matrices (necessarily consistent)
for(j in 1:3){
x=runif(2,min=-1,max=1); y=runif(2,min=-1,max=1)
SigmaS[[j]]=cov2cor(x%*%t(x) + y%*%t(y))
}
X1 = mvrnorm(m, c(1,0), SigmaS[[1]])
X2 = mvrnorm(m, c(0,0), SigmaS[[2]])
X3 = mvrnorm(m, c(3,0), SigmaS[[3]])
columns = c("X1","X2","X3")
X = data.frame(matrix(nrow = 3*m, ncol = 3))
X[1:m, c("X1", "X2")] = X1
X[(m+1):(2*m), c("X2", "X3")] = X2
X[(2*m+1):(3*m), c("X1", "X3")] = X3
X = as.matrix(X)
meanConsTest(X, B)
[Package MCARtest version 1.3 Index]