Description Usage Arguments Value References See Also Examples
View source: R/FwerMethodsSD.R
Bonferroni multiple testing method for correlations with stepdown procedure.
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data |
matrix of observations |
alpha |
level of multiple testing |
stat_test |
4 test statistics are available:
|
vect |
if TRUE returns a vector of TRUE/FALSE 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
if FALSE, returns a list of successive p-values : element [[i+1]] of the list giving the p-values evaluated on the non-rejected hypothesis at step [[i]]; p-values are either as a vector or a list depending on |
arr.ind |
if TRUE, returns the indexes of the significant correlations, with respect to level alpha |
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 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
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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:
BonferroniCor_SD(data,alpha,stat_test='empirical', arr.ind=TRUE)
# successive p-values
res <- BonferroniCor_SD(data,stat_test='empirical', logical=FALSE)
lapply(res,FUN=function(x){round(x,2)})
# succesive rejections
lapply(res,FUN=function(x){whichCor(x<alpha)})
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