6.30 G03 Multivariate Methods

g03 - Multivariate Methods

g03 Chapter Introduction

Routine Name Mark of Introduction Purpose
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_orthomax
Orthogonal rotations for loading matrix
g03bcc 5 nag_mv_procustes
Procrustes rotations
g03bdc 9 nag_mv_promax
ProMax rotations
g03cac 5 nag_mv_factor
Maximum likelihood estimates of parameters
g03ccc 5 nag_mv_fac_score
Factor score coefficients, following nag_mv_factor (g03cac)
g03dac 5 nag_mv_discrim
Test for equality of within-group covariance matrices
g03dbc 5 nag_mv_discrim_mahaldist
Mahalanobis squared distances, following nag_mv_discrim (g03dac)
g03dcc 5 nag_mv_discrim_group
Allocates observations to groups, following nag_mv_discrim (g03dac)
g03eac 5 nag_mv_distance_mat
Compute distance (dissimilarity) matrix
g03ecc 5 nag_mv_hierar_cluster_analysis
Hierarchical cluster analysis
g03efc 5 nag_mv_kmeans_cluster_analysis
K-means
g03ehc 5 nag_mv_dendrogram
Construct dendogram following nag_mv_hierar_cluster_analysis (g03ecc)
g03ejc 5 nag_mv_cluster_indicator
Construct clusters following nag_mv_hierar_cluster_analysis (g03ecc)
g03fac 5 nag_mv_prin_coord_analysis
Principal co-ordinate analysis
g03fcc 5 nag_mv_ordinal_multidimscale
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 nag_mv_dendrogram (g03ehc)
g03zac 5 nag_mv_z_scores
Standardize values of a data matrix