We have tried to break down a typical process of multivariate data analysis, in
trying to identify key components. We then built a fully object-oriented toolbox,
with an object fitting each of those key components.
. We have identified three entities, which are the building
blocks of any multivariate data process. The sampleset
carries information about the different samples, also called observations, conditions, or experiments.
object carries information about labeling
of the samples, i.e., their association with specific clusters. The measurements themselves are in
matrix. The datamatrix
object is the general
framework of a datamatrix, from which more specialized data matrices are derived by object-oriented
inheritence. These more specialized data matrices encompass most of the data organization forms
one may encounter. The vsmatrix
a rectangular two-way matrix of variables-by-samples. For example, a result of a gene
array experiment in the form of genes-by-conditions will be represented in our toolbox by a
object. The ssmatrix
relationships between samples. For example, a distance matrix will be represented in our toolbox
as a ssmatrix
. The vvmatrix
relationships between variables. For example, a correlation matrix will be represented in our
toolbox as a vvmatrix
. The graph
describes a general mathematical graph. This toolbox includes more specific graphs
(such as digraphs
) that are derived from this general object.
Pairwise data objects
. These objects describe specific
forms of pairwise data, and are all derived from either ssmatrix
(see above). The covmatrix
object describes a covariance or a correlation matrix. The
object describes a distance matrix.
objects describe pairwise dissimilarity
or similarity information, respectively.
Dimensionality reduction algorithms
. Each object in this group
stands for a particular dimensionality reduction technique. Currently available are
that makes principal component
analysis (PCA), wpcatrans
weighted principal component analysis (wPCA), and
that identifies discriminant
direction according to the Fisher linear discriminant analysis.
. This portion of the toolbox includes
general statistical functions, mainly various hypothesis testing procedures, as
well as the object ctable
a contingency table.
Navigate to: General Description
List of Objects
List of Functions
The toolbox is freely available from this site. The latest release is MVA_13Sep2010
the toolbox General Utilities
To recieve updates, new releases, etc, please subscribe below:
To stop recieving emails with updates, new releases, etc, please unsubscribe below: