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 |