maxTinftyCor_SD

0th

Percentile

Multiple testing method of Drton & Perlman (2007) for correlations, with stepdown procedure.

Multiple testing method based on the evaluation of quantile by simulation of observations from the asymptotic distribution (Drton & Perlman (2007)), with stepdown procedure.

Usage
maxTinftyCor_SD(data, alpha = 0.05, stat_test = "empirical",
  Nboot = 1000, OmegaChap = covDcorNorm(cor(data), stat_test),
  vect = FALSE)
Arguments
data

matrix of observations

alpha

level of multiple testing

stat_test
'empirical'

\(\sqrt{n}*abs(corr)\)

'fisher'

\(\sqrt{n-3}*1/2*\log( (1+corr)/(1-corr) )\)

'student'

\(\sqrt{n-2}*abs(corr)/\sqrt(1-corr^2)\)

Notice that 'gaussian' is not available.

Nboot

number of iterations for Monte-Carlo quantile evaluation

OmegaChap

matrix of covariance of test statistics; optional, useful for oracle estimation and step-down

vect

if TRUE returns a vector of TRUE/FALSE values, corresponding to vectorize(cor(data)); if FALSE, returns an array containing rows and columns of significative correlations

Value

Returns

  • a vector containing indexes \(\lbrace(i,j),\,i<j\rbrace\) for which correlation between variables \(i\) and \(j\) is significative, if vect=FALSE.

References

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., Universit<U+00E9> Grenoble Alpes, France, https://tel.archives-ouvertes.fr/tel-01971574v1.

See Also

ApplyFwerCor, maxTinftyCor

Aliases
  • maxTinftyCor_SD
Examples
# NOT RUN {
 
n <- 100
p <- 10
corr_theo <- diag(1,p)
data <- MASS::mvrnorm(n,rep(0,p),corr_theo)
alpha <- 0.05
res <- maxTinftyCor_SD(data,alpha,stat_test='empirical',Nboot=1000)
# }
Documentation reproduced from package TestCor, version 0.0.0.9, License: GPL (>= 2)

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