SimuFwer
Simulates Gaussian data with a given correlation matrix and applies a FWER controlling procedure on the correlations.
Simulates Gaussian data with a given correlation matrix and applies a FWER controlling procedure on the correlations.
Usage
SimuFwer(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
- 'empirical'
\(\sqrt{n}*abs(corr)\)
- 'fisher'
\(\sqrt{n-3}*1/2*\log( (1+corr)/(1-corr) )\)
- 'student'
\(\sqrt{n-2}*abs(corr)/\sqrt(1-corr^2)\)
- 'gaussian'
\(\sqrt{n}*mean(Y)/sd(Y)\) 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
Value
Returns a line vector containing estimated fwer, estimated fdr, estimated power, estimated true discovery rate.
References
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., Universit<U+00E9> 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.
See Also
ApplyFwerCor, SimuFwer_oracle, SimuFdr
Examples
# NOT RUN {
Nsimu <- 1000
n <- 100
p <- 10
corr_theo <- diag(1,p)
alpha <- 0.05
res <- SimuFwer(corr_theo,n,Nsimu,alpha,stat_test='empirical',method='Bonferroni',stepdown=FALSE)
# }