SimuFdr {TestCor} | R Documentation |
Simulates Gaussian data with a given correlation matrix and applies a FDR controlling procedure on the correlations.
Description
Simulates Gaussian data with a given correlation matrix and applies a FDR controlling procedure on the correlations.
Usage
SimuFdr(
corr_theo,
n = 100,
Nsimu = 1,
alpha = 0.05,
stat_test = "empirical",
method = "LCTnorm",
Nboot = 1000,
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 |
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 |
Value
Returns a line vector containing estimated values for fwer, fdr, sensitivity, specificity and accuracy.
References
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.
See Also
ApplyFdrCor, SimuFwer
Examples
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')