# Bonferroni multiple testing procedure for correlations. ```r 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`: - **'empirical'**: $\sqrt{n}*abs(corr)$ - **'fisher'**: $\sqrt{n-3}*1/2*\log( (1+corr)/(1-corr) )$ - **'student'**: $\sqrt{n-2}*abs(corr)/\sqrt(1-corr^2)$ - **'2nd.order'**: $\sqrt{n}*mean(Y)/sd(Y)$ 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 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\

BonferroniCor function