Several standard multivariate methods are provided by Genstat directives. These include methods that analyse data in the form of units-by-variates, and methods that use a similarity or distance matrix.

The following directives carry out standard multivariate analyses:

`CVA` |
canonical variates analysis |
---|---|

`FCA` |
factor analysis |

`MDS` |
non-metric multidimensional scaling |

`PCP` |
principal components analysis |

`PCO` |
principal coordinates analysis |

`ROTATE` |
Procrustes rotation |

Other directives and procedures are available to process results from multivariate analyses:

`ADDPOINTS` |
adds points for new objects to a `PCO` |
---|---|

`CVAPLOT` |
plots the mean and unit scores from a canonical variates analysis |

`CVASCORES` |
calculates scores for individual units in canonical variates analysis |

`CVATRELLIS` |
displays the distribution of groups over 2 dimensions from a `CVA` analysis using a trellis of bar or pie charts |

`DBIPLOT` |
plots a biplot from an analysis by `PCP` , `CVA` or `PCO` |

`DMST` |
gives a high resolution plot of an ordination with minimum spanning tree |

`FACROTATE` |
rotates factor loadings from a `PCP` , `CVA` or `FCA` `ADDPOINTS` |

`LRVSCREE` |
prints a scree diagram and/or a difference table of latent roots |

`PCORELATE` |
relates principal coordinates to original data variables |

The following commands carry out hierarchical and non-hierarchical cluster analysis:

`CLUSTER` |
non-hierarchical clustering from a data matrix |
---|---|

`FSIMILARITY` |
forms a similarity matrix or a between-group similarity matrix from a units-by-variates data matrix |

`HREDUCE` |
forms a reduced similarity matrix (by groups) |

`HCLUSTER` |
hierarchical cluster analysis from a similarity matrix |

` PCPCLUSTER` |
forms groups of units using the densities of their `PCP` scores |

`PTFCLUSTERS` |
forms clusters of points from their densities in multi-dimensional space |

Other directives and procedures that process the results from cluster analyses are:

`DDENDROGRAM` |
draws dendrograms with control over structure and style |
---|---|

`DCLUSTERLABELS` |
labels clusters in a single-page dendrogram plotted by `DDENDROGRAM` |

`HBOOTSTRAP` |
performs bootstrap analyses to assess the reliability of clusters from hierarchical cluster analysis |

`HCOMPAREGROUPINGS` |
compares groupings generated, for example, from cluster analyses |

`HDISPLAY` |
displays results associated with hierarchical clustering |

`HFAMALGAMATIONS` |
forms an amalgamations |

`HFCLUSTERS` |
forms a set of clusters from an amalgamations matrix |

`HLIST` |
lists a data matrix in abbreviated form |

`HPCLUSTERS` |
prints a set of clusters |

`HSUMMARIZE` |
summarizes data variates by clusters |

`PTFILLCLUSTERS` |
fills holes within clusters of points in multi-dimensional space |

Other multivariate techniques are provided by procedures in the Library:

`AMMI` |
allows exploratory analysis of genotype × environment interactions |
---|---|

`BCLASSIFICATION` |
constructs a classification tree |

`BCDISPLAY` |
displays a classification tree |

`BCIDENTIFY` |
identifies specimens using a classification tree |

`BCKEEP` |
saves information from a classification tree |

`BCVALUES` |
forms values for nodes of a classification tree |

`BCFOREST` |
constructs a random classification forest |

`BCFDISPLAY` |
displays information about a random classification forest |

`BCFIDENTIFY` |
identifies specimens using a random classification forest |

`BIPLOT` |
produces a biplot from a set of variates |

`BKEY` |
constructs an identification key |

`BKDISPLAY` |
displays an identification key |

`BKIDENTIFY` |
identifies specimens using a key |

`BKKEEP` |
saves information from an identification key |

`CANCORRELATION` |
does canonical correlation analysis |

`CCA` |
performs canonical correspondence analysis |

`CRBIPLOT` |
plots correlation or distance biplots after `CCA` or `RDA` |

`CRTRIPLOT` |
plots ordination biplots or triplots after `CCA` or `RDA` |

`CINTERACTION` |
clusters rows and columns of a two-way interaction table |

`CLASSIFY` |
obtains a starting classification for non-hierarchical clustering |

`CONVEXHULL` |
finds the points of a single or a full peel of convex-hulls |

`CORANALYSIS` |
does correspondence analysis, or reciprocal averaging |

`MCORANALYSIS` |
does multiple correspondence analysis |

`CABIPLOT` |
plots results from correspondence analysis or multiple correspondence analysis |

`DISCRIMINATE` |
performs discriminant analysis |

`SDISCRIMINATE` |
selects the best set of variates to discriminate between groups |

`QDISCRIMINATE` |
performs quadratic discrimination between groups i.e. allowing for different variance-covariance matrices |

`DPARALLEL` |
displays multivariate data using parallel coordinates |

`GESTABILITY` |
calculates stability coefficients for genotype-by-environment data |

`GGEBIPLOT` |
plots displays to assess genotype + genotype-by-environment variation |

`GENPROCRUSTES` |
performs a generalized Procrustes analysis |

`IDENTIFY` |
identifies an unknown specimen from a defined set of objects |

`KNEARESTNEIGHBOURS` |
classifies items or predicts their responses by examining their k nearest neighbours |

`MANOVA` |
performs multivariate analysis of variance and covariance |

`MANTEL` |
assesses the association between similarity matrices |

`MULTMISSING` |
estimates missing values for units in a multivariate data set |

`MVAOD` |
does an analysis of distance of multivariate data |

`NORMTEST` |
performs tests of univariate and/or multivariate normality |

`OPLS` |
performs orthogonal partial least squares regression |

`PCOPROCRUSTES` |
performs a multiple Procrustes analysis |

`PLS` |
fits a partial least squares regression model |

`RDA` |
performs redundancy analysis |

`RIDGE` |
produces ridge regression and principal component regression analyses |

`LRIDGE` |
does logistic ridge regression |

`RLFUNCTIONAL` |
fits a linear functional relationship model |

`RMULTIVARIATE` |
performs multivariate linear regression with accumulated testing of terms |

`ROBSSPM` |
forms robust estimates of sum-of-squares-and-products matrices |

`SAGRAPES` |
produces statistics and graphs for checking sensory panel performance |

`SKEWSYMMETRY` |
provides an analysis of skew-symmetry for an asymmetric matrix |