A collection of PCA methods

Documentation for package ‘pcaMethods’ version 1.96.0

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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.