BonferroniCor {TestCor} | R Documentation |
Bonferroni multiple testing procedure for correlations.
Description
Bonferroni multiple testing procedure for correlations.
Usage
BonferroniCor(
data,
alpha = 0.05,
stat_test = "empirical",
vect = FALSE,
logical = FALSE,
arr.ind = FALSE
)
Arguments
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 |
Value
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 variablesi
andj
is significant, ifarr.ind=TRUE
.
References
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.
See Also
ApplyFwerCor, BonferroniCor_SD
Examples
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)