Description Usage Arguments Value References See Also Examples
Bonferroni multiple testing procedure for correlations.
1 2 3 4 5 6 7 8 |
data |
matrix of observations |
alpha |
level of multiple testing (used if logical=TRUE) |
stat_test |
|
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 |
arr.ind |
if TRUE, returns the indexes of the significant correlations, with respect to level alpha |
Returns
the adjusted p-values, as a vector or a matrix depending of the value of 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
.
Bonferroni, C. E. (1935). Il calcolo delle assicurazioni su gruppi di teste. Studi in onore del professore salvatore ortu carboni, 13-60.
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, BonferroniCor_SD
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | n <- 100
p <- 10
corr_theo <- diag(1,p)
corr_theo[1,3] <- 0.5
corr_theo[3,1] <- 0.5
corr_theo <- diag(1,p)
data <- MASS::mvrnorm(n,rep(0,p),corr_theo)
# adjusted p-values
res <- BonferroniCor(data,stat_test='empirical')
round(res,2)
# significant correlations with level alpha:
alpha <- 0.05
whichCor(res<alpha)
# directly
BonferroniCor(data,alpha,stat_test='empirical',arr.ind=TRUE)
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