| Name : R-HDMD
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| Version : 1.2
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.2.13
| Date : 2024-06-14 11:25:14
|
| Group : Development/Libraries/Other
| Source RPM : R-HDMD-1.2-lp153.2.13.src.rpm
|
| Size : 0.11 MB
| |
| Packager : (none)
| |
| Summary : Statistical Analysis Tools for High Dimension Molecular Data (HDMD)
|
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 prcomp and princomp do
not reflect the proportion of total variation of each principal
component. 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 factor.pa of the psych package. The 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,
factor.pa.ginv removes and warns of any variables that are constant,
which would otherwise create an invalid covariance matrix. 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. 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. MolecularEntropy and 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 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. FactorTransform converts amino acid
sequences using the amino acid indices determined by Atchley et al
2005.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
