Returns statistics.
public double[] GetStatistics();
Return Value
A double array containing output statistics.
Remarks
Statistics are not defined and set to NaN when the method used to obtain the estimates is the principal component method, principal factor method, image factor analysis method, or alpha analysis method.
| i | Statistics[i] |
|---|---|
| 0 | Value of the function minimum. |
| 1 | Tucker reliability coefficient. |
| 2 | Chi-squared test statistic for testing that the number of factors in the model are adequate for the data. |
| 3 | Degrees of freedom in chi-squared. This is computed as
nvar is the number of variables and nfactors is the number of factors in the model. |
| 4 | Probability of a greater chi-squared statistic. |
| 5 | Number of iterations. |
Exceptions
| 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. |
See Also
FactorAnalysis Class | Imsl.Stat Namespace