A B C D E F G H K L M N O P Q R S T V W
asExprSet | Convert pcaRes object to an expression set |
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 |
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... |
completeObs-method | Get the original data with missing values replaced with predicted... |
cvstat | Get cross-validation statistics (e... |
cvstat-method | Get cross-validation statistics (e... |
deletediagonals | Delete diagonals |
derrorHierarchic | Later... |
dim.pcaRes | Dimensions of a PCA model... |
DModX | DModX |
DModX-method | DModX |
errorHierarchic | Later... |
fitted-method | Fitted PCA data. |
fitted.pcaRes | Extract fitted values from PCA. |
forkNlpcaNet | Complete copy of nlpca net object... |
getHierarchicIdx | Index in hiearchy... |
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-method | Extract leverages of a PCA model |
lineSearch | Line search for conjugate gradient... |
linr | Linear kernel... |
listPcaMethods | List PCA methods |
llsImpute | LLSimpute algorithm |
loadings.pcaRes | Get the loadings from a PCA model... |
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... |
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. |
optiAlgCgd | Conjugate gradient optimization... |
orth | Calculate an orthonormal basis |
pca | Perform principal component analysis |
pcaMethods | pcaMethods |
pcaMethods-deprecated | Deprecated methods for pcaMethods |
pcaRes | Class for representing a PCA result |
pcaRes-class | Class for representing a PCA result |
plot.pcaRes | Plot diagnostics (screeplot) |
plotPcs | Plot many side by side scores XOR loadings plots |
plotR2 | R2 plot (screeplot) for PCA |
ppca | Probabilistic PCA |
predict-method | Predict PCA data. |
predict.pcaRes | Predict values from PCA. |
prep | Pre-process a matrix for PCA |
print-method | Print basic info... |
Q2 | Cross-validation for PCA |
R2cum | Cumulative R2 is the total ratio of variance that is being... |
R2cum-method | Cumulative R2 is the total ratio of variance that is being... |
repmat | Replicate and tile an array. |
resid-method | 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 |
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... |
scl-method | Get the scales (e... |
scores.pcaRes | Get the scores from a PCA model... |
sDev | Get the standard deviations of the scores (indicates their... |
sDev-method | Get the standard deviations of the scores (indicates their... |
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 |
svdImpute | SVDimpute algorithm |
svdPca | Perform principal component analysis using singular value decomposition |
tempFixNas | Temporary fix for missing values |
vector2matrices-method | Tranform the vectors of weights to matrix structure... |
wasna | Get a matrix with indicating the elements that were missing in the... |
wasna-method | Get a matrix with indicating the elements that were missing in the... |
weightsAccount | Create an object that holds the weights for nlpcaNet. |