BootRWCor {TestCor} | R Documentation |
Bootstrap multiple testing method of Romano & Wolf (2005) for correlations.
Description
Multiple testing method based on the evaluation of quantile by bootstrap in the initial dataset (Romano & Wolf (2005)).
Usage
BootRWCor(
data,
alpha = 0.05,
stat_test = "empirical",
Nboot = 1000,
vect = FALSE,
logical = FALSE,
arr.ind = FALSE
)
Arguments
data |
matrix of observations |
alpha |
level of multiple testing (used if logical=TRUE) |
stat_test |
|
Nboot |
number of iterations for Monte-Carlo quantile evaluation |
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
Romano, J. P., & Wolf, M. (2005). Exact and approximate stepdown methods for multiple hypothesis testing. Journal of the American Statistical Association, 100(469), 94-108.
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, BootRWCor_SD
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)
# adjusted p-values
res <- BootRWCor(data,stat_test='empirical',Nboot=1000)
round(res,2)
# significant correlations with level alpha:
alpha <- 0.05
whichCor(res<alpha)
# directly
BootRWCor(data,alpha,stat_test='empirical',Nboot=1000,arr.ind=TRUE)