# SidakCor

0th

Percentile

##### Sidak multiple testing procedure for correlations.

Sidak multiple testing procedure for correlations.

##### Usage
SidakCor(data, alpha = 0.05, stat_test = "empirical", vect = FALSE)
##### Arguments
data

matrix of observations

alpha

level of multiple testing

stat_test
'empirical'

$\sqrt{n}*abs(corr)$

'fisher'

$\sqrt{n-3}*1/2*\log( (1+corr)/(1-corr) )$

'student'

$\sqrt{n-2}*abs(corr)/\sqrt(1-corr^2)$

'gaussian'

$\sqrt{n}*mean(Y)/sd(Y)$ with $Y=(X_i-mean(X_i))(X_j-mean(X_j))$

vect

if TRUE returns a vector of TRUE/FALSE values, corresponding to vectorize(cor(data)); if FALSE, returns an array containing rows and columns of significative correlations

##### Value

Returns

• a vector containing indexes $\lbrace(i,j),\,i<j\rbrace$ for which correlation between variables $i$ and $j$ is significative, if vect=FALSE.

##### References

Roux, M. (2018). Graph inference by multiple testing with application to Neuroimaging, Ph.D., Universit<U+00E9> Grenoble Alpes, France, https://tel.archives-ouvertes.fr/tel-01971574v1.

<U+0160>id<U+00E1>k, Z. (1967). Rectangular confidence regions for the means of multivariate normal distributions. Journal of the American Statistical Association, 62(318), 626-633.

ApplyFwerCor, SidakCor_SD

• SidakCor
##### Examples
# NOT RUN {

n <- 100
p <- 10
corr_theo <- diag(1,p)
data <- MASS::mvrnorm(n,rep(0,p),corr_theo)
corr_mat <- cor(data)
corr_vect <- corr_mat[upper.tri(corr_mat)]
alpha <- 0.05
res <- SidakCor(data,alpha,stat_test='empirical')
# }

Documentation reproduced from package TestCor, version 0.0.0.9, License: GPL (>= 2)

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