Routine |
Mark of Introduction |
Purpose |
|---|---|---|
| g03aaf | 14 | nagf_mv_prin_comp Performs principal component analysis |
| g03acf | 14 | nagf_mv_canon_var Performs canonical variate analysis |
| g03adf | 14 | nagf_mv_canon_corr Performs canonical correlation analysis |
| g03baf | 15 | nagf_mv_rot_orthomax Computes orthogonal rotations for loading matrix, generalized orthomax criterion |
| g03bcf | 15 | nagf_mv_rot_procrustes Computes Procrustes rotations |
| g03bdf | 22 | nagf_mv_rot_promax ProMax rotations |
| g03caf | 15 | nagf_mv_factor Computes maximum likelihood estimates of the parameters of a factor analysis model, factor loadings, communalities and residual correlations |
| g03ccf | 15 | nagf_mv_factor_score Computes factor score coefficients (for use after g03caf) |
| g03daf | 15 | nagf_mv_discrim Computes test statistic for equality of within-group covariance matrices and matrices for discriminant analysis |
| g03dbf | 15 | nagf_mv_discrim_mahal Computes Mahalanobis squared distances for group or pooled variance-covariance matrices (for use after g03daf) |
| g03dcf | 15 | nagf_mv_discrim_group Allocates observations to groups according to selected rules (for use after g03daf) |
| g03eaf | 16 | nagf_mv_distance_mat Computes distance matrix |
| g03ecf | 16 | nagf_mv_cluster_hier Hierarchical cluster analysis |
| g03eff | 16 | nagf_mv_cluster_kmeans -means cluster analysis |
| g03ehf | 16 | nagf_mv_cluster_hier_dendrogram Constructs dendrogram (for use after g03ecf) |
| g03ejf | 16 | nagf_mv_cluster_hier_indicator Computes cluster indicator variable (for use after g03ecf) |
| g03faf | 17 | nagf_mv_multidimscal_metric Performs principal coordinate analysis, classical metric scaling |
| g03fcf | 17 | nagf_mv_multidimscal_ordinal Performs non-metric (ordinal) multidimensional scaling |
| g03gaf | 24 | nagf_mv_gaussian_mixture Fits a Gaussian mixture model |
| g03gbf | 29 | nagf_mv_gaussian_mixture_ld Fits a Gaussian mixture model with results stored in submatrices |
| g03zaf | 15 | nagf_mv_z_scores Produces standardized values (-scores) for a data matrix |