A B C D E F G H K L M N O P Q R S T V W
pcaMethods-package | pcaMethods |
asExprSet | Convert pcaRes object to an expression set |
biplot-method | Plot a overlaid scores and loadings plot |
biplot-methods | Plot a overlaid scores and loadings plot |
biplot.pcaRes | Plot a overlaid scores and loadings plot |
bpca | Bayesian PCA missing value estimation |
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 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). |
deletediagonals | Delete diagonals |
derrorHierarchic | Later |
dim.pcaRes | Dimensions of a PCA model |
DModX | DModX |
DModX-method | DModX |
errorHierarchic | Later |
fitted-method | Extract fitted values from PCA. |
fitted-methods | Extract fitted values from PCA. |
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 | Crude way to unmask the function with the same name from 'stats' |
loadings-method | Crude way to unmask the function with the same name from 'stats' |
loadings-method | Get loadings from a pcaRes object |
loadings.pcaRes | Get loadings from a pcaRes object |
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 |
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 |
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-method | Predict values from PCA. |
predict-methods | Predict values from PCA. |
predict.pcaRes | Predict values from PCA. |
prep | Pre-process a matrix for PCA |
print-method | Print/Show for pcaRes |
Q2 | Cross-validation for PCA |
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 |
rediduals-methods | Residuals values from a PCA model. |
repmat | Replicate and tile an array. |
resid-method | Residuals values from a PCA model. |
residuals-method | Residuals values from a PCA model. |
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.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-method | Print/Show for pcaRes |
show-methods | Print/Show for pcaRes |
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 |
tempFixNas | Temporary fix for missing values |
vector2matrices-method | Tranform the vectors of weights to matrix structure |
vector2matrices-method | Tranform the vectors of weights to matrix structure |
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. |