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