Package: HDMD
Type: Package
Title: Statistical Analysis Tools for High Dimension Molecular Data
        (HDMD)
Version: 1.0
Date: 2009-11-03
Author: Lisa McFerrin
Maintainer: Lisa McFerrin <lgmcferr@ncsu.edu>
Depends: psych, MASS, base
Suggests: scatterplot3d
Description: High Dimensional Molecular Data (HDMD) typically have many
        more variables or dimensions than observations or replicates
        (D>>N).  This can cause many statistical procedures to fail,
        become intractable, or produce misleading results.  This
        package provides several tools to reduce dimensionality and
        analyze biological data for meaningful interpretation of
        results. Factor Analysis (FA), Principal Components Analysis
        (PCA) and Discriminant Analysis (DA) are frequently used
        multivariate techniques.  However, PCA methods
        \code{\link{prcomp}} and \code{\link{princomp}} do not reflect
        the proportion of total variation of each principal component.
        \code{\link{Loadings.variation}} displays the relative and
        cumulative contribution of variation for each component by
        accounting for all variability in data. When D>>N, the maximum
        likelihood method cannot be applied in FA and the the principal
        axes method must be used instead, as in \code{\link{factor.pa}}
        of the \code{\link{psych}} package. The
        \code{\link{factor.pa.ginv}} function in this package further
        allows for a singular covariance matrix by applying a general
        inverse method to estimate factor scores.  Moreover,
        \code{\link{factor.pa.ginv}} removes and warns of any variables
        that are constant, which would otherwise create an invalid
        covariance matrix. \code{\link{Promax.only}} further allows
        users to define rotation parameters during factor estimation.
        Similar to the Euclidean distance, the Mahalanobis distance
        estimates the relationship among groups.
        \code{\link{pairwise.mahalanobis}} computes all such pairwise
        Mahalanobis distances among groups and is useful for
        quantifying the separation of groups in DA. Genetic sequences
        are composed of discrete alphabetic characters, which makes
        estimates of variability difficult.
        \code{\link{MolecularEntropy}} and \code{\link{MolecularMI}}
        calculate the entropy and mutual information to estimate
        variability and covariability, respectively, of DNA or Amino
        Acid sequences.  Functional grouping of amino acids (Atchley et
        al 1999) is also available for entropy and mutual information
        estimation.  Mutual information values can be normalized by
        \code{\link{NMI}} to account for the background distribution
        arising from the stochastic pairing of independent, random
        sites. Alternatively, discrete alphabetic sequences can be
        transformed into biologically informative metrics to be used in
        various multivariate procedures.  \code{\link{FactorTransform}}
        converts amino acid sequences using the amino acid indices
        determined by Atchley et al 2005.
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2009-11-04 18:18:58 UTC; Lisa
Repository: CRAN
Date/Publication: 2009-11-05 12:34:14
