Finds in a computationally fast algorithm the adjusted average square correlation magnitude for every variable of a dataset.
1  cor2mean.adj(mat)

mat 
p \times n matrix with the pvariate dataset. 
The adjusted average square correlation of variable i is given by
(n1)/(n2) \bar{r}_{i}^2  1/(n2)
where n is the sample size and \bar{r}_{i}^2 is the average square
correlation matrix for the ith row, which is computed by cor2mean
.
A vector containing the adjusted square average correlation (excluding diagonal) for every variable.
Mayer, Claus, Adria Caballe and Natalia Bochkina.
To come
cor2mean
for average square correlations.
1 2 3 4  EX1 < pcorSimulator(nobs = 50, nclusters= 3, nnodesxcluster = c(100,30,50),
pattern = "powerLaw", plus = 0)
corsEX1 < cor2mean(t(EX1$y))
corsadjEX1 < cor2mean.adj(t(EX1$y))

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