Q2                      Perform internal cross-validation for PCA
asExprSet               Convert pcaRes object to an expression set
bpca                    Bayesian PCA Missing Value Estimator
checkData               Do some basic checks on a given data matrix
fitted.pcaRes           Extract fitted values from PCA.
helix                   A helix structured toy data set
kEstimate               Estimate best number of Components for missing
                        value estimation
kEstimateFast           Estimate best number of Components for missing
                        value estimation
llsImpute               LLSimpute algorithm
metaboliteData          An incomplete metabolite data set from an
                        Arabidopsis coldstress experiment
metaboliteDataComplete
                        A complete metabolite data set from an
                        Arabidopsis coldstress experiment
nipalsPca               Perform principal component analysis using the
                        Non-linear iterative partial least squares
                        (NIPALS) algorithm.
nlpca                   Non-linear PCA
nlpcaNet                Class for representing a neural network for
                        computing Non-linear PCA
nni                     Nearest neighbour imputation
nniRes                  Class for representing a nearest neighbour
                        imputation result
pca                     Perform principal component analysis
pcaRes                  Class for representing a PCA result
plotPcs                 Plot many side by side scores XOR loadings
                        plots
plotR2                  R2 plot (screeplot) for PCA
ppca                    Probabilistic PCA Missing Value Estimator
prep                    Preprocess a matrix for PCA
robustPca               PCA implementation based on robustSvd
robustSvd               Alternating L1 Singular Value Decomposition
slplot                  Plot a side by side scores and loadings plot
svdImpute               SVDimpute algorithm
svdPca                  Perform principal component analysis using
                        singular value decomposition
