SimuFwer: 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 FWER controlling procedure on the correlations.

SimuFwer(
  corr_theo,
  n = 100,
  Nsimu = 1,
  alpha = 0.05,
  stat_test = "empirical",
  method = "Sidak",
  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`	choice between 'Bonferroni', 'Sidak', 'BootRW', 'MaxTinfty'
`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.

Bonferroni, C. E. (1935). Il calcolo delle assicurazioni su gruppi di teste. Studi in onore del professore salvatore ortu carboni, 13-60.

Drton, M., & Perlman, M. D. (2007). Multiple testing and error control in Gaussian graphical model selection. Statistical Science, 22(3), 430-449.

Romano, J. P., & Wolf, M. (2005). Exact and approximate stepdown methods for multiple hypothesis testing. Journal of the American Statistical Association, 100(469), 94-108.

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.

Westfall, P.H. & Young, S. (1993) Resampling-based multiple testing: Examples and methods for p-value adjustment, John Wiley & Sons, vol. 279.

ApplyFwerCor, SimuFwer_oracle, SimuFdr

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
SimuFwer(corr_theo,n,Nsimu,alpha,stat_test='empirical',method='Bonferroni',stepdown=FALSE)