Returns the correlations of the principal components.
A double matrix containing the correlations of the principal components with the observed/standardized variables.
If a covariance matrix is input to the constructor, then the correlations are with the observed variables. Otherwise, the correlations are with the standardized (to a variance of 1.0) variables. Only valid for the Principal Components Model.
| Exception Type | Condition |
|---|---|
| RankException | is thrown if the rank of the covariance matrix is less than the number of factors. |
| NoDegreesOfFreedomException | is thrown if there are no degrees of freedom for the significance testing. |
| NotSemiDefiniteException | is thrown if the Hessian matrix not semi-definite. |
| NotPositiveSemiDefiniteException | is thrown if the covariance matrix is not positive semi-definite. |
| NotPositiveDefiniteException | is thrown if the covariance matrix is not positive definite because a variable is linearly releated to other variables. |
| SingularException | is thrown if the covariance matrix is singular. |
| BadVarianceException | is thrown if the input variance is not in the allowed range. |
| EigenvalueException | is thrown if an error occured in calculating the eigenvalues of the adjusted (inverse) covariance matrix. Check the input covariance matrix. |
| NonPositiveEigenvalueException | is thrown if in alpha factor analysis an eigenvalue is not positive. As all eigenvalues corresponding to the factors must be positive, either the number of factors must be reduced, or new initial estimates for the unique variances must be given. |
FactorAnalysis Class | Imsl.Stat Namespace