A collection of PCA methods

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Documentation for package ‘pcaMethods’ version 1.64.0

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A B C D E F G H K L M N O P Q R S T V W

pcaMethods-package pcaMethods

-- A --

asExprSet Convert pcaRes object to an expression set

-- B --

biplot-method Plot a overlaid scores and loadings plot
biplot-methods Plot a overlaid scores and loadings plot
biplot.pcaRes Plot a overlaid scores and loadings plot
bpca Bayesian PCA missing value estimation
BPCA_dostep Do BPCA estimation step
BPCA_initmodel Initialize BPCA model

-- C --

center Get the centers of the original variables
center-method Get the centers of the original variables
centered Check centering was part of the model
centered-method Check centering was part of the model
checkData Do some basic checks on a given data matrix
completeObs Get the original data with missing values replaced with predicted values.
completeObs-method Get the original data with missing values replaced with predicted values.
cvseg Get CV segments
cvstat Get cross-validation statistics (e.g. Q^2).
cvstat-method Get cross-validation statistics (e.g. Q^2).

-- D --

deletediagonals Delete diagonals
derrorHierarchic Later
dim.pcaRes Dimensions of a PCA model
DModX DModX
DModX-method DModX

-- E --

errorHierarchic Later

-- F --

fitted-method Extract fitted values from PCA.
fitted-methods Extract fitted values from PCA.
fitted.pcaRes Extract fitted values from PCA.
forkNlpcaNet Complete copy of nlpca net object

-- G --

getHierarchicIdx Index in hiearchy

-- H --

helix A helix structured toy data set

-- K --

kEstimate Estimate best number of Components for missing value estimation
kEstimateFast Estimate best number of Components for missing value estimation

-- L --

leverage Extract leverages of a PCA model
leverage-method Extract leverages of a PCA model
lineSearch Line search for conjugate gradient
linr Linear kernel
listPcaMethods List PCA methods
llsImpute LLSimpute algorithm
loadings Crude way to unmask the function with the same name from 'stats'
loadings-method Crude way to unmask the function with the same name from 'stats'
loadings-method Get loadings from a pcaRes object
loadings.pcaRes Get loadings from a pcaRes object

-- M --

metaboliteData A incomplete metabolite data set from an Arabidopsis coldstress experiment
metaboliteDataComplete A complete metabolite data set from an Arabidopsis coldstress experiment
method Get the used PCA method
method-method Get the used PCA method

-- N --

nipalsPca NIPALS PCA
nlpca Non-linear PCA
nlpcaNet Class representation of the NLPCA neural net
nlpcaNet-class Class representation of the NLPCA neural net
nmissing Missing values
nmissing-method Missing values
nni Nearest neighbour imputation
nniRes Class for representing a nearest neighbour imputation result
nniRes-class Class for representing a nearest neighbour imputation result
nObs Get the number of observations used to build the PCA model.
nObs-method Get the number of observations used to build the PCA model.
nP Get number of PCs
nP-method Get number of PCs
nPcs Get number of PCs.
nPcs-method Get number of PCs.
nVar Get the number of variables used to build the PCA model.
nVar-method Get the number of variables used to build the PCA model.

-- O --

optiAlgCgd Conjugate gradient optimization
orth Calculate an orthonormal basis

-- P --

pca Perform principal component analysis
pcaMethods pcaMethods
pcaMethods-deprecated Deprecated methods for pcaMethods
pcaNet Class representation of the NLPCA neural net
pcaRes Class for representing a PCA result
pcaRes-class Class for representing a PCA result
plot-method Plot diagnostics (screeplot)
plot.pcaRes Plot diagnostics (screeplot)
plotPcs Plot many side by side scores XOR loadings plots
ppca Probabilistic PCA
predict-method Predict values from PCA.
predict-methods Predict values from PCA.
predict.pcaRes Predict values from PCA.
prep Pre-process a matrix for PCA
print-method Print/Show for pcaRes

-- Q --

Q2 Cross-validation for PCA

-- R --

R2cum Cumulative R2 is the total ratio of variance that is being explained by the model
R2cum-method Cumulative R2 is the total ratio of variance that is being explained by the model
R2VX R2 goodness of fit
R2VX-method R2 goodness of fit
rediduals-methods Residuals values from a PCA model.
repmat Replicate and tile an array.
resid-method Residuals values from a PCA model.
residuals-method Residuals values from a PCA model.
residuals.pcaRes Residuals values from a PCA model.
RnipalsPca NIPALS PCA implemented in R
robustPca PCA implementation based on robustSvd
robustSvd Alternating L1 Singular Value Decomposition

-- S --

scaled Check if scaling was part of the PCA model
scaled-method Check if scaling was part of the PCA model
scl Get the scales (e.g. standard deviations) of the original variables
scl-method Get the scales (e.g. standard deviations) of the original variables
scores Get scores from a pcaRes object
scores-method Get scores from a pcaRes object
scores.pcaRes Get scores from a pcaRes object
sDev Get the standard deviations of the scores (indicates their relevance)
sDev-method Get the standard deviations of the scores (indicates their relevance)
show-method Print/Show for pcaRes
show-methods Print/Show for pcaRes
showNniRes Print a nniRes model
showPcaRes Print/Show for pcaRes
simpleEllipse Hotelling's T^2 Ellipse
slplot Side by side scores and loadings plot
slplot-method Side by side scores and loadings plot
sortFeatures Sort the features of NLPCA object
summary Summary of PCA model
summary-method Summary of PCA model
summary.pcaRes Summary of PCA model
svdImpute SVDimpute algorithm
svdPca Perform principal component analysis using singular value decomposition

-- T --

tempFixNas Temporary fix for missing values

-- V --

vector2matrices-method Tranform the vectors of weights to matrix structure
vector2matrices-method Tranform the vectors of weights to matrix structure

-- W --

wasna Get a matrix with indicating the elements that were missing in the input data. Convenient for estimating imputation performance.
wasna-method Get a matrix with indicating the elements that were missing in the input data. Convenient for estimating imputation performance.
weightsAccount Create an object that holds the weights for nlpcaNet. Holds and sets weights in using an environment object.