maxTinftyCor_SD: Multiple testing method of Drton & Perlman (2007) for correlations, with stepdown procedure.
Description
Multiple testing method based on the evaluation of quantile by simulation of observations
from the asymptotic distribution (Drton & Perlman (2007)),
with stepdown procedure.
number of iterations for Monte-Carlo quantile evaluation
OmegaChap
matrix of covariance of test statistics;
optional, useful for oracle estimation and step-down
vect
if TRUE returns a vector of TRUE/FALSE values, corresponding to vectorize(cor(data));
if FALSE, returns an array containing rows and columns of significative correlations
Value
Returns
a vector containing indexes \(\lbrace(i,j),\,i<j\rbrace\) for which correlation between variables \(i\) and \(j\) is significative, if vect=FALSE.
References
Drton, M., & Perlman, M. D. (2007). Multiple testing and error control in Gaussian graphical model selection. Statistical Science, 22(3), 430-449.
Roux, M. (2018). Graph inference by multiple testing with application to Neuroimaging, Ph.D., Universit<U+00E9> Grenoble Alpes, France, https://tel.archives-ouvertes.fr/tel-01971574v1.
# NOT RUN {
n <- 100
p <- 10
corr_theo <- diag(1,p)
data <- MASS::mvrnorm(n,rep(0,p),corr_theo)
alpha <- 0.05
res <- maxTinftyCor_SD(data,alpha,stat_test='empirical',Nboot=1000)
# }