SimuFwer_oracle {TestCor} | R Documentation |
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.
Usage
SimuFwer_oracle(
corr_theo,
n = 100,
Nsimu = 1,
alpha = 0.05,
stat_test = "empirical",
method = "MaxTinfty",
Nboot = 1000,
stepdown = TRUE,
seed = NULL
)
Arguments
corr_theo |
the correlation matrix of Gaussien data simulated |
n |
sample size |
Nsimu |
number of simulations |
alpha |
level of multiple testing |
stat_test |
|
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 values for fwer, fdr, sensitivity, specificity and accuracy.
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., Université Grenoble Alpes, France, https://tel.archives-ouvertes.fr/tel-01971574v1.
See Also
ApplyFwerCor_Oracle, SimuFwer
Examples
Nsimu <- 1000
n <- 50
p <- 10
corr_theo <- diag(1,p)
corr_theo[1,3] <- 0.5
corr_theo[3,1] <- 0.5
alpha <- 0.05
SimuFwer_oracle(corr_theo,n,Nsimu,alpha,stat_test='empirical',stepdown=FALSE,Nboot=100)