Description

Calculate the indicator correlation matrix using conventional or robust methods.

Usage

Arguments

Details

If .approach_cor_robust = "none" (the default) the type of correlation computed depends on the types of the columns of .X_cleaned (i.e., the indicators) involved in the computation.

Numeric-numeric

If both columns (indicators) involved are numeric, the Bravais-Pearson product-moment correlation is computed (via stats::cor()).

Numeric-factor

If any of the columns is a factor variable, the polyserial correlation (Drasgow 1988) is computed (via polycor::hetcor()).

Factor-factor

If both columns are factor variables, the polychoric correlation (Drasgow 1988) is computed (via polycor::hetcor()).

Note: logical input is treated as a 0-1 factor variable.

If "mcd" (= minimum covariance determinant), the MCD estimator (Rousseeuw and Driessen 1999), a robust covariance estimator, is applied (via MASS::cov.rob()).

If "spearman", the Spearman rank correlation is used (via stats::cor()).

Value

A list with elements:

$S

The (K x K) indicator correlation matrix

$cor_type

The type(s) of indicator correlation computed ( "Pearson", "Polyserial", "Polychoric")

$thre_est

Currently ignored (NULL)

References

Drasgow F (1988). “Polychoric and polyserial correlations.” In Encyclopedia of Statistical Sciences, volume 7, 68–74. John Wiley & Sons Inc, Hoboken.

Rousseeuw PJ, Driessen KV (1999). “A Fast Algorithm for the Minimum Covariance Determinant Estimator.” Technometrics, 41(3), 212–223. doi: 10.1080/00401706.1999.10485670, https://doi.org/10.1080/00401706.1999.10485670.