Description Usage Arguments Value References See Also Examples
Applies oracle MaxTinfty procedure described in Drton & Perlman (2007) which controls asymptotically the FWER for tests on a correlation matrix. It needs the true correlation matrix.
1 2 3 4 5 6 7 8 9 10 11 12 |
data |
matrix of observations |
corr_theo |
true matrix of correlations |
alpha |
level of multiple testing (used if logical=TRUE) |
stat_test |
|
method |
only 'MaxTinfty' implemented |
Nboot |
number of iterations for Monte-Carlo of bootstrap quantile evaluation |
stepdown |
logical, if TRUE a stepdown procedure is applied |
vect |
if TRUE returns a vector of adjusted p-values, corresponding to |
logical |
if TRUE, returns either a vector or a matrix where each element is equal to TRUE if the corresponding null hypothesis is rejected, and to FALSE if it is not rejected
if |
arr.ind |
if TRUE, returns the indexes of the significant correlations, with repspect to level alpha |
Returns either
the adjusted p-values, as a vector or a matrix, depending on vect
(unavailable with stepdown)
logicals indicating if the corresponding correlation is significant if logical=TRUE
, as a vector or a matrix depending on vect
,
an array containing indexes \lbrace(i,j),\,i<j\rbrace for which correlation between variables i and j is significant, if arr.ind=TRUE
.
Oracle estimation of the quantile is used, based on the true correlation matrix
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., Université Grenoble Alpes, France, https://tel.archives-ouvertes.fr/tel-01971574v1.
ApplyFwerCor
maxTinftyCor, maxTinftyCor_SD
1 2 3 4 5 6 7 8 9 10 11 | n <- 100
p <- 10
corr_theo <- diag(1,p)
corr_theo[1,3] <- 0.5
corr_theo[3,1] <- 0.5
data <- MASS::mvrnorm(n,rep(0,p),corr_theo)
# adjusted p-values:
(res <- ApplyFwerCor_oracle(data,corr_theo,stat_test='empirical',Nboot=1000,stepdown=FALSE))
# significant correlations, level alpha:
alpha <- 0.05
whichCor(res<alpha)
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