SimuFwer_oracle: Simulates Gaussian data with a given correlation matrix and applies oracle MaxTinfty on the correlations.
Description
Simulates Gaussian data with a given correlation matrix and applies oracle MaxTinfty (i.e. Drton & Perlman (2007)'s procedure with the true correlation matrix) on the correlations.
\(\sqrt{n}*mean(Y)/sd(Y)\) with \(Y=(X_i-mean(X_i))(X_j-mean(X_j))\)
method
only 'MaxTinfty' available
Nboot
number of iterations for Monte-Carlo of bootstrap quantile evaluation
stepdown
logical, if TRUE a stepdown procedure is applied
seed
seed for the Gaussian simulations
Value
Returns a line vector containing estimated fwer, estimated fdr, estimated power, estimated true discovery rate.
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 {Nsimu <- 1000
n <- 50
p <- 10
corr_theo <- diag(1,p)
alpha <- 0.05
res <- SimuFwer_oracle(corr_theo,n,Nsimu,alpha,stat_test='empirical',stepdown=FALSE,Nboot=100)
# }