pcaMethods v1.64.0

A collection of PCA methods

Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany.



R package for performing principal component analysis PCA with applications to missing value imputation. Provides a single interface to performing PCA using

  • SVD: a fast method which is also the standard method in R but which is not applicable for data with missing values.
  • NIPALS: an iterative fast method which is applicable also to data with missing values.
  • PPCA: Probabilistic PCA which is applicable also on data with missing values. Missing value estimation is typically better than NIPALS but also slower to compute and uses more memory. A port to R of the implementation by Jakob Verbeek.
  • BPCA: Bayesian PCA which performs very well in the presence of missing values but is slower than PPCA. A port of the matlab implementation by Shigeyuki Oba.
  • NLPCA: Non-linear PCA which can find curves in data and in presence of such can perform accurate missing value estimation. Matlab port of the implementation by Mathias Scholz.

pcaMethods is a Bioconductor package and you can install it by




Functions in pcaMethods

Name Description
metaboliteDataComplete A complete metabolite data set from an Arabidopsis coldstress experiment
nipalsPca NIPALS PCA
R2cum,pcaRes-method Cumulative R2 is the total ratio of variance that is being explained by the model
helix A helix structured toy data set
loadings,ANY-method Crude way to unmask the function with the same name from stats
Q2 Cross-validation for PCA
deletediagonals Delete diagonals
checkData Do some basic checks on a given data matrix
asExprSet Convert pcaRes object to an expression set
errorHierarchic Later
nVar,pcaRes-method Get the number of variables used to build the PCA model.
center,pcaRes-method Get the centers of the original variables
cvstat,pcaRes-method Get cross-validation statistics (e.g. $Q^2$).
simpleEllipse Hotelling's T^2 Ellipse
pcaNet Class representation of the NLPCA neural net
predict-methods Predict values from PCA.
biplot-methods Plot a overlaid scores and loadings plot
linr Linear kernel
pcaRes Class for representing a PCA result
R2VX,pcaRes-method R2 goodness of fit
derrorHierarchic Later
showNniRes Print a nniRes model
nlpca Non-linear PCA
pca Perform principal component analysis
lineSearch Line search for conjugate gradient
llsImpute LLSimpute algorithm
scl,pcaRes-method Get the scales (e.g. standard deviations) of the original variables
nP,pcaRes-method Get number of PCs
DModX,pcaRes-method DModX
method,pcaRes-method Get the used PCA method
dim.pcaRes Dimensions of a PCA model
fitted-methods Extract fitted values from PCA.
nPcs,pcaRes-method Get number of PCs.
ppca Probabilistic PCA
BPCA_dostep Do BPCA estimation step
plotPcs Plot many side by side scores XOR loadings plots
loadings,pcaRes-method Get loadings from a pcaRes object
summary Summary of PCA model
sDev,pcaRes-method Get the standard deviations of the scores (indicates their relevance)
robustSvd Alternating L1 Singular Value Decomposition
show-methods Print/Show for pcaRes
leverage,pcaRes-method Extract leverages of a PCA model
pcaMethods-deprecated Deprecated methods for pcaMethods
weightsAccount Create an object that holds the weights for nlpcaNet. Holds and sets weights in using an environment object.
scores.pcaRes Get scores from a pcaRes object
vector2matrices,matrix-method Tranform the vectors of weights to matrix structure
metaboliteData A incomplete metabolite data set from an Arabidopsis coldstress experiment
pcaMethods pcaMethods
tempFixNas Temporary fix for missing values
optiAlgCgd Conjugate gradient optimization
plot.pcaRes Plot diagnostics (screeplot)
orth Calculate an orthonormal basis
vector2matrices,nlpcaNet-method Tranform the vectors of weights to matrix structure
svdImpute SVDimpute algorithm
svdPca Perform principal component analysis using singular value decomposition
nni Nearest neighbour imputation
repmat Replicate and tile an array.
scaled,pcaRes-method Check if scaling was part of the PCA model
wasna,pcaRes-method Get a matrix with indicating the elements that were missing in the input data. Convenient for estimating imputation performance.
centered,pcaRes-method Check centering was part of the model
cvseg Get CV segments
robustPca PCA implementation based on robustSvd
nObs,pcaRes-method Get the number of observations used to build the PCA model.
loadings.pcaRes Get loadings from a pcaRes object
bpca Bayesian PCA missing value estimation
BPCA_initmodel Initialize BPCA model
RnipalsPca NIPALS PCA implemented in R
kEstimate Estimate best number of Components for missing value estimation
kEstimateFast Estimate best number of Components for missing value estimation
nmissing,pcaRes-method Missing values
nniRes Class for representing a nearest neighbour imputation result
sortFeatures Sort the features of NLPCA object
prep Pre-process a matrix for PCA
scores,pcaRes-method Get scores from a pcaRes object
getHierarchicIdx Index in hiearchy
forkNlpcaNet Complete copy of nlpca net object
completeObs,nniRes-method Get the original data with missing values replaced with predicted values.
listPcaMethods List PCA methods
slplot,pcaRes-method Side by side scores and loadings plot
rediduals-methods Residuals values from a PCA model.
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License GPL (>= 3)
LinkingTo Rcpp
LazyLoad Yes
SystemRequirements Rcpp
URL https://github.com/hredestig/pcamethods
BugReports https://github.com/hredestig/pcamethods/issues
Collate 'derrorHierarchic.R' 'errorHierarchic.R' 'AllClasses.R' 'AllGenerics.R' 'BPCA_dostep.R' 'BPCA_initmodel.R' 'bpca.R' 'checkData.R' 'forkNlpcaNet.R' 'kEstimate.R' 'kEstimateFast.R' 'lineSearch.R' 'llsImpute.R' 'methods-ExpressionSet.R' 'methods-nniRes.R' 'methods-pcaRes.R' 'nipalsPca.R' 'nlpca.R' 'optiAlgCgd.R' 'orth.R' 'pca.R' 'pcaMethods-package.R' 'ppca.R' 'prep.R' 'repmat.R' 'robustPca.R' 'sortFeatures.R' 'svdImpute.R' 'vector2matrices.R' 'xval.R'
Packaged 2013-03-12 21:41:03 UTC; henning
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