Multivariate and cluster analysis
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:
canonical variates analysis
principal components analysis
principal coordinates analysis
Procrustes rotation
non-metric multidimensional scaling
Separate directives are available to process results from multivariate analyses:
rotates factor loadings from a
PCP or
CVA
adds points for new objects to a
PCO
relates principal coordinates to original data variates
The following directives are used for hierarchical or non-hierarchical cluster analysis:
forms a similarity matrix or a between-group similarity matrix
from a units-by-variates data matrix
forms a reduced similarity matrix (by groups)
hierarchical cluster analysis from a similarity matrix
non-hierarchical clustering from a data matrix
Separate directives that process the results from hierarchical cluster analyses are:
displays results associated with hierarchical clustering
lists a data matrix in abbreviated form
summarizes data variates by clusters
Other multivariate techniques are provided by procedures in the mva module of the Library:
allows exploratory analysis of genotype × environment
interactions
constructs a classification tree
displays a classification tree
identifies specimens using a classification tree
forms values for nodes of a classification tree
produces a biplot from a set of variates
constructs an identification key
displays an identification key
identifies specimens using a key
identifies an unknown specimen from a defined set of
objects
does canonical correlation analysis
clusters rows and columns of a two-way interaction
table
obtains a starting classification for non-hierarchical
clustering
finds the points of a single or a full peel of convex-hulls
does correspondence analysis, or reciprocal averaging
(synonym CORRESP)
plots the mean and unit scores from a canonical variates
analysis
calculates scores for individual units in canonical variates
analysis
draws dendrograms with control over structure and style
performs discriminant analysis
gives a high resolution plot of an ordination with minumum spanning
tree
displays multivariate data using parallel coordinates
performs a generalized Procrustes analysis
prints a scree diagram and/or a difference table of latent
roots
performs multivariate analysis of variance and covariance
assesses the association between similarity matrices
estimates missing values for units in a multivariate data
set
performs tests of univariate and/or multivariate normality
performs a multiple Procrustes analysis
fits a partial least squares regression model
produces ridge regression and principal component regression
analyses
fits a linear functional relationship model
performs multivariate linear regression with accumulated
testing of terms (synonym FITMULTIVARIATE)
forms robust estimates of sum-of-squares-and-products
matrices
provides an analysis of skew-symmetry for an asymmetric
matrix