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G03 (Mv)
Multivariate Methods

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G03 (Mv) Chapter Introduction – A description of the Chapter and an overview of the algorithms available.

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g03aac 5 nag_mv_prin_comp
Principal component analysis
g03acc 5 nag_mv_canon_var
Canonical variate analysis
g03adc 5 nag_mv_canon_corr
Canonical correlation analysis
g03bac 5 nag_mv_rot_orthomax
Orthogonal rotations for loading matrix
g03bcc 5 nag_mv_rot_procrustes
Procrustes rotations
g03bdc 9 nag_mv_rot_promax
ProMax rotations
g03cac 5 nag_mv_factor
Maximum likelihood estimates of parameters
g03ccc 5 nag_mv_factor_score
Factor score coefficients, following g03cac
g03dac 5 nag_mv_discrim
Test for equality of within-group covariance matrices
g03dbc 5 nag_mv_discrim_mahal
Mahalanobis squared distances, following g03dac
g03dcc 5 nag_mv_discrim_group
Allocates observations to groups, following g03dac
g03eac 5 nag_mv_distance_mat
Compute distance (dissimilarity) matrix
g03ecc 5 nag_mv_cluster_hier
Hierarchical cluster analysis
g03efc 5 nag_mv_cluster_kmeans
g03ehc 5 nag_mv_cluster_hier_dendrogram
Construct dendogram following g03ecc
g03ejc 5 nag_mv_cluster_hier_indicator
Construct clusters following g03ecc
g03fac 5 nag_mv_multidimscal_metric
Principal coordinate analysis
g03fcc 5 nag_mv_multidimscal_ordinal
Multidimensional scaling
g03gac 24 nag_mv_gaussian_mixture
Fits a Gaussian mixture model
g03xzc 5 nag_mv_dend_free
Frees memory allocated to the dendrogram array in g03ehc
g03zac 5 nag_mv_z_scores
Standardize values of a data matrix