BonferroniCor: Bonferroni multiple testing procedure for correlations.
In TestCor: FWER and FDR Controlling Procedures for Multiple Correlation Tests

Description Usage Arguments Value References See Also Examples

Bonferroni multiple testing procedure for correlations.

BonferroniCor(
  data,
  alpha = 0.05,
  stat_test = "empirical",
  vect = FALSE,
  logical = FALSE,
  arr.ind = FALSE
)

`data`	matrix of observations
`alpha`	level of multiple testing (used if logical=TRUE)
`stat_test`	'empirical' √{n}abs(corr)* 'fisher' √{n-3}1/2\log( (1+corr)/(1-corr) ) 'student' √{n-2}abs(corr)/√(1-corr^2)* '2nd.order' √{n}mean(Y)/sd(Y)* with Y=(X_i-mean(X_i))(X_j-mean(X_j))
`vect`	if TRUE returns a vector of adjusted p-values, corresponding to `vectorize(cor(data))`; if FALSE, returns an array containing the adjusted p-values for each entry of the correlation matrix
`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

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)