BHCor {TestCor} | R Documentation |
Benjamini & Hochberg (1995)'s procedure for correlation testing.
Description
Benjamini & Hochberg (1995)'s procedure on the correlation matrix entries (no theoretical proof of control).
Usage
BHCor(
data,
alpha = 0.05,
stat_test = "2nd.order",
vect = FALSE,
arr.ind = FALSE
)
Arguments
data |
matrix of observations |
alpha |
level of multiple testing |
stat_test |
|
vect |
if TRUE returns a vector of TRUE/FALSE values, corresponding to vectorize(cor(data)) if FALSE, returns an array containing TRUE/FALSE values for each entry of the correlation matrix |
arr.ind |
if TRUE, returns the indexes of the significant correlations, with respect to level alpha |
Value
Returns
logicals, equal to TRUE if the corresponding element of the statistic vector is rejected, 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
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society. Series B (Methodological), 289-300.
See Also
ApplyFdrCor, BHBootCor
Examples
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)
alpha <- 0.05
# significant correlations:
BHCor(data,alpha,stat_test='empirical',arr.ind=TRUE)