Package: pcaMethods 1.99.0

Henning Redestig

pcaMethods: 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.

Authors:Wolfram Stacklies, Henning Redestig, Kevin Wright

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pcaMethods.pdf |pcaMethods.html✨
pcaMethods/json (API)

# Install 'pcaMethods' in R:
install.packages('pcaMethods', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/hredestig/pcamethods/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • helix - A helix structured toy data set
  • metaboliteData - A incomplete metabolite data set from an Arabidopsis coldstress experiment
  • metaboliteDataComplete - A complete metabolite data set from an Arabidopsis coldstress experiment

On BioConductor:pcaMethods-1.97.0(bioc 3.20)pcaMethods-1.96.0(bioc 3.19)

bayesian

13.02 score 45 stars 71 packages 502 scripts 8.1k downloads 129 mentions 44 exports 4 dependencies

Last updated 18 days agofrom:8148736e3b. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-win-x86_64NOTEOct 30 2024
R-4.5-linux-x86_64NOTEOct 30 2024
R-4.4-win-x86_64NOTEOct 30 2024
R-4.4-mac-x86_64NOTEOct 30 2024
R-4.4-mac-aarch64NOTEOct 30 2024
R-4.3-win-x86_64NOTEOct 30 2024
R-4.3-mac-x86_64NOTEOct 30 2024
R-4.3-mac-aarch64NOTEOct 30 2024

Exports:asExprSetbpcacentercenteredcheckDatacompleteObscvsegcvstatDModXkEstimatekEstimateFastleveragelistPcaMethodsllsImputeloadingsmethodnipalsPcanlpcanmissingnninObsnPnPcsnVarpcaplotPcsppcaprepQ2R2cumR2VXRnipalsPcarobustPcarobustSvdscaledsclscoressDevshowNniResshowPcaResslplotsvdImputesvdPcawasna

Dependencies:BiobaseBiocGenericsMASSRcpp

Data with outliers

Rendered fromoutliers.Rnwusingutils::Sweaveon Oct 30 2024.

Last update: 2023-09-25
Started: 2012-06-14

Introduction

Rendered frompcaMethods.Rnwusingutils::Sweaveon Oct 30 2024.

Last update: 2023-09-25
Started: 2012-06-14

Missing value imputation

Rendered frommissingValues.Rnwusingutils::Sweaveon Oct 30 2024.

Last update: 2012-06-14
Started: 2012-06-14

Readme and manuals

Help Manual

Help pageTopics
Convert pcaRes object to an expression setasExprSet
Plot a overlaid scores and loadings plotbiplot,pcaRes-method biplot-methods biplot.pcaRes
Bayesian PCA missing value estimationbpca
Do BPCA estimation stepBPCA_dostep
Initialize BPCA modelBPCA_initmodel
Get the centers of the original variablescenter center,pcaRes-method
Check centering was part of the modelcentered centered,pcaRes-method
Do some basic checks on a given data matrixcheckData
Get the original data with missing values replaced with predicted values.completeObs completeObs,nniRes-method completeObs,pcaRes-method
Get CV segmentscvseg
Get cross-validation statistics (e.g. Q^2).cvstat cvstat,pcaRes-method
Delete diagonalsdeletediagonals
LaterderrorHierarchic
Dimensions of a PCA modeldim.pcaRes
DModXDModX DModX,pcaRes-method
LatererrorHierarchic
Extract fitted values from PCA.fitted,pcaRes-method fitted-methods fitted.pcaRes
Complete copy of nlpca net objectforkNlpcaNet
Index in hiearchygetHierarchicIdx
A helix structured toy data sethelix
Estimate best number of Components for missing value estimationkEstimate
Estimate best number of Components for missing value estimationkEstimateFast
Extract leverages of a PCA modelleverage leverage,pcaRes-method
Line search for conjugate gradientlineSearch
Linear kernellinr
List PCA methodslistPcaMethods
LLSimpute algorithmllsImpute
Crude way to unmask the function with the same name from 'stats'loadings loadings,ANY-method
Get loadings from a pcaRes objectloadings,pcaRes-method
Get loadings from a pcaRes objectloadings.pcaRes
A incomplete metabolite data set from an Arabidopsis coldstress experimentmetaboliteData
A complete metabolite data set from an Arabidopsis coldstress experimentmetaboliteDataComplete
Get the used PCA methodmethod method,pcaRes-method
NIPALS PCAnipalsPca
Non-linear PCAnlpca
Missing valuesnmissing nmissing,nniRes-method nmissing,pcaRes-method
Nearest neighbour imputationnni
Class for representing a nearest neighbour imputation resultnniRes nniRes-class
Get the number of observations used to build the PCA model.nObs nObs,pcaRes-method
Get number of PCsnP nP,pcaRes-method
Get number of PCs.nPcs nPcs,pcaRes-method
Get the number of variables used to build the PCA model.nVar nVar,pcaRes-method
Conjugate gradient optimizationoptiAlgCgd
Calculate an orthonormal basisorth
Perform principal component analysispca
pcaMethodspcaMethods-package pcaMethods
Deprecated methods for pcaMethodspcaMethods-deprecated
Class representation of the NLPCA neural netnlpcaNet nlpcaNet-class pcaNet
Class for representing a PCA resultpcaRes pcaRes-class
Plot diagnostics (screeplot)plot,pcaRes-method plot.pcaRes
Plot many side by side scores XOR loadings plotsplotPcs
Probabilistic PCAppca
Predict values from PCA.predict,pcaRes-method predict-methods predict.pcaRes
Pre-process a matrix for PCAprep
Cross-validation for PCAQ2
Cumulative R2 is the total ratio of variance that is being explained by the modelR2cum R2cum,pcaRes-method
R2 goodness of fitR2VX R2VX,pcaRes-method
Residuals values from a PCA model.rediduals-methods resid,pcaRes-method residuals,pcaRes-method residuals.pcaRes
Replicate and tile an array.repmat
NIPALS PCA implemented in RRnipalsPca
PCA implementation based on robustSvdrobustPca
Alternating L1 Singular Value DecompositionrobustSvd
Check if scaling was part of the PCA modelscaled scaled,pcaRes-method
Get the scales (e.g. standard deviations) of the original variablesscl scl,pcaRes-method
Get scores from a pcaRes objectscores scores,pcaRes-method
Get scores from a pcaRes objectscores.pcaRes
Get the standard deviations of the scores (indicates their relevance)sDev sDev,pcaRes-method
Print/Show for pcaResprint,nniRes-method print,pcaRes-method show,nniRes-method show,pcaRes-method show-methods showPcaRes
Print a nniRes modelshowNniRes
Hotelling's T^2 EllipsesimpleEllipse
Side by side scores and loadings plotslplot slplot,pcaRes-method
Sort the features of NLPCA objectsortFeatures
Summary of PCA modelsummary summary,pcaRes-method summary.pcaRes
SVDimpute algorithmsvdImpute
Perform principal component analysis using singular value decompositionsvdPca
Temporary fix for missing valuestempFixNas
Tranform the vectors of weights to matrix structurevector2matrices,matrix-method
Tranform the vectors of weights to matrix structurevector2matrices,nlpcaNet-method
Get a matrix with indicating the elements that were missing in the input data. Convenient for estimating imputation performance.wasna wasna,pcaRes-method
Create an object that holds the weights for nlpcaNet. Holds and sets weights in using an environment object.weightsAccount