ldstatsHD (version 1.0.1)

cor2mean.adj: adjusted average square correlation by rows

Description

Finds in a computationally fast algorithm the adjusted average square correlation magnitude for every variable of a dataset.

Usage

cor2mean.adj(mat)

Arguments

mat

\(p \times n\) matrix with the p-variate dataset.

Value

A vector containing the adjusted square average correlation (excluding diagonal) for every variable.

Details

The adjusted average square correlation of variable \(i\) is given by $$ (n-1)/(n-2) \bar{r}_{i}^2 - 1/(n-2) $$ where \(n\) is the sample size and \(\bar{r}_{i}^2\) is the average square correlation matrix for the \(i\)th row, which is computed by cor2mean.

References

To come

See Also

cor2mean for average square correlations.

Examples

# NOT RUN {
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))

# }