Class L12 ... Discriminant analysis

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Modules for class L12

• DDMSCR (DMSCR) Use Fisher's linear discriminant analysis method to reduce the number of variabl ...
• DDSCRM (DSCRM) Perform a linear or a quadratic discriminant function analysis among several kno ...
• DMSCR (DDMSCR) Use Fisher's linear discriminant analysis method to reduce the number of variabl ...
• DNGHBR (NGHBR) Search a k-d tree for the k nearest neighbors of a key.
• DNNBRD (NNBRD) K nearest neighbor discrimination.
• DQUADT (QUADT) Form a k-d tree.
• DSCRM (DDSCRM) Perform a linear or a quadratic discriminant function analysis among several kno ...
• NGHBR (DNGHBR) Search a k-d tree for the k nearest neighbors of a key.
• NNBRD (DNNBRD) K nearest neighbor discrimination.
• QUADT (DQUADT) Form a k-d tree.

• G03ACF Performs canonical variate analysis.
• G03DAF Computes a test statistic for the equality of within-group covariance matrices a ...
• G03DBF Computes Mahalanobis squared distances for group or pooled variance-covariance m ...
• G03DCF Allocates observations to groups according to selected rules. It is intended for ...
• G03ZAF Produces standardized values (z-scores) for a data matrix.

• NAG_MV_CANON_VAR Performs a canonical variate (canonical descrimination) analysis.
• NAG_MV_DISCRIM_GROUP Allocates observations to groups according to selected rules. It is intended for ...
• NAG_MV_DISCRIM_MAHALDIST Computes Mahalanobis squared distances for group or pooled variance-covariance m ...
• NAG_MV_DISCRIM Computes a test statistic for the equality of within-group covariance matrices a ...

• CANDISC Performs a canonical discriminant analysis, computes Mahalanobis distances, and ...
• DISCRIM Computes linear or quadratic discriminant functions for classifying observations ...
• NEIGHBOR Performs a nearest neighbor discriminant analysis, classifying observations into ...
• STEPDISC Performs a stepwise discriminant analysis by forward selection, backward elimina ...