SimuFwer_oracle: Simulates Gaussian data with a given correlation matrix and...
In TestCor: FWER and FDR Controlling Procedures for Multiple Correlation Tests

Description Usage Arguments Value References See Also Examples

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.

SimuFwer_oracle(
  corr_theo,
  n = 100,
  Nsimu = 1,
  alpha = 0.05,
  stat_test = "empirical",
  method = "MaxTinfty",
  Nboot = 1000,
  stepdown = TRUE,
  seed = NULL
)

`corr_theo`	the correlation matrix of Gaussien data simulated
`n`	sample size
`Nsimu`	number of simulations
`alpha`	level of multiple testing
`stat_test`	'empirical' √{n}abs(corr)* 'fisher' √{n-3}1/2\log( (1+corr)/(1-corr) ) 'student' √{n-2}abs(corr)/√(1-corr^2)* 'gaussian' √{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

Returns a line vector containing estimated values for fwer, fdr, sensitivity, specificity and accuracy.

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.

ApplyFwerCor_Oracle, SimuFwer

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)