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| asExprSet | Convert pcaRes object to an expression set |
| biplot,pcaRes-method | Plot a overlaid scores and loadings plot |
| biplot.pcaRes | Plot a overlaid scores and loadings plot |
| bpca | Bayesian PCA Missing Value Estimator |
| centered | Class for representing a PCA result |
| centered,pcaRes-method | Class for representing a PCA result |
| checkData | Do some basic checks on a given data matrix |
| completeObs | Class for representing a PCA result |
| completeObs,pcaRes-method | Class for representing a PCA result |
| dim.pcaRes | Class for representing a PCA result |
| fitted,pcaRes-method | Extract fitted values from PCA. |
| 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 |
| leverage | Extract leverages of a PCA model |
| leverage,pcaRes-method | Class for representing a PCA result |
| llsImpute | LLSimpute algorithm |
| loadings.pcaRes | Class for representing a PCA result |
| metaboliteData | An incomplete metabolite data set from an Arabidopsis coldstress experiment |
| metaboliteDataComplete | A complete metabolite data set from an Arabidopsis coldstress experiment |
| method | Class for representing a PCA result |
| method,pcaRes-method | Class for representing a PCA result |
| 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 |
| nlpcaNet-class | Class for representing a neural network for computing Non-linear PCA |
| nni | Nearest neighbour imputation |
| nniRes | Class for representing a nearest neighbour imputation result |
| nniRes-class | Class for representing a nearest neighbour imputation result |
| nObs | Class for representing a PCA result |
| nObs,pcaRes-method | Class for representing a PCA result |
| nPcs | Class for representing a PCA result |
| nPcs,pcaRes-method | Class for representing a PCA result |
| nVar | Class for representing a PCA result |
| nVar,pcaRes-method | Class for representing a PCA result |
| pca | Perform principal component analysis |
| pcaRes | Class for representing a PCA result |
| pcaRes-class | 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 |
| predict,pcaRes-method | Predict values from PCA. |
| predict.pcaRes | Predict values from PCA. |
| prep | Preprocess a matrix for PCA |
| print,nniRes-method | Class for representing a nearest neighbour imputation result |
| print,pcaRes-method | Class for representing a PCA result |
| Q2 | Perform internal cross-validation for PCA |
| residuals,pcaRes-method | Residuals values from a PCA model. |
| residuals.pcaRes | Residuals values from a PCA model. |
| robustPca | PCA implementation based on robustSvd |
| robustSvd | Alternating L1 Singular Value Decomposition |
| scores.pcaRes | Class for representing a PCA result |
| sDev | Class for representing a PCA result |
| sDev,pcaRes-method | Class for representing a PCA result |
| show,pcaRes-method | Class for representing a PCA result |
| slplot | Plot a side by side scores and loadings plot |
| slplot,pcaRes-method | Class for representing a PCA result |
| summary,pcaRes-method | Class for representing a PCA result |
| svdImpute | SVDimpute algorithm |
| svdPca | Perform principal component analysis using singular value decomposition |