SimuFdr: 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 a FDR controlling procedure on the correlations.

SimuFdr(
  corr_theo,
  n = 100,
  Nsimu = 1,
  alpha = 0.05,
  stat_test = "empirical",
  method = "LCTnorm",
  Nboot = 1000,
  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`	choice between 'LCTnorm' and 'LCTboot', developped by Cai & Liu (2016), 'BH', traditional Benjamini-Hochberg (1995)'s procedure, and 'BHboot', Benjamini-Hochberg (1995)'s procedure with bootstrap evaluation of pvalues
`Nboot`	number of iterations for Monte-Carlo of bootstrap quantile evaluation
`seed`	seed for the Gaussian simulations

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

Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society. Series B (Methodological), 289-300.

Cai, T. T., & Liu, W. (2016). Large-scale multiple testing of correlations. Journal of the American Statistical Association, 111(513), 229-240.

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.

ApplyFdrCor, SimuFwer

Nsimu <- 1000
n <- 100
p <- 10
corr_theo <- diag(1,p)
corr_theo[1,3] <- 0.5
corr_theo[3,1] <- 0.5
alpha <- 0.05
SimuFdr(corr_theo,n,Nsimu,alpha,stat_test='empirical',method='LCTnorm')