# A collection of PCA methods.

## Documentation for package ‘pcaMethods’ version 1.56.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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 | Biplot for pcaRes method.
biplot-method | Biplot for pcaRes method.
biplot.pcaRes | Plot a overlaid scores and loadings plot
bpca | Bayesian PCA Missing Value Estimator
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 | Fitted PCA data.
fitted-method | Fitted PCA data.
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 | Get loadings from a pcaRes object
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 --

nFit | Class representation of the NLPCA neural net
nFit-class | Class representation of the NLPCA neural net
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 | Predict PCA data.
predict-method | Predict PCA data.
predict.pcaRes | Predict values from PCA.
prep | Pre-process a matrix for PCA
print | Print basic info
print-method | Print basic info

## -- 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
repmat | Replicate and tile an array.
resid | Residuals of PCA data.
resid-method | Residuals of PCA data.
residuals | Residuals of PCA data.
residuals-method | Residuals of PCA data.
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 | Show pcaRes / nniRes objects.
show-method | Show pcaRes / nniRes objects.
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 | 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.