pcaMethods

Do BPCA estimation step

Initialize BPCA model

DModX

Cross-validation for PCA

R2 goodness of fit

Cumulative R2 is the total ratio of variance that is being explained by the model

NIPALS PCA implemented in R

Convert pcaRes object to an expression set

Plot a overlaid scores and loadings plot

Bayesian PCA missing value estimation

Get the centers of the original variables

Check centering was part of the model

Do some basic checks on a given data matrix

Get the original data with missing values replaced with predicted values.

Get CV segments

Get cross-validation statistics (e.g. Q^2).

Delete diagonals

Later

Dimensions of a PCA model

Later

Extract fitted values from PCA.

Complete copy of nlpca net object

Index in hiearchy

A helix structured toy data set

Estimate best number of Components for missing value estimation

Estimate best number of Components for missing value estimation

Extract leverages of a PCA model

Line search for conjugate gradient

Linear kernel

List PCA methods

LLSimpute algorithm

Crude way to unmask the function with the same name from stats

Get loadings from a pcaRes object

Get loadings from a pcaRes object

A incomplete metabolite data set from an Arabidopsis coldstress experiment

A complete metabolite data set from an Arabidopsis coldstress experiment

Get the used PCA method

Get the number of observations used to build the PCA model.

Get number of PCs

Get number of PCs.

Get the number of variables used to build the PCA model.

NIPALS PCA

Non-linear PCA

Missing values

Nearest neighbour imputation

Class for representing a nearest neighbour imputation result

Conjugate gradient optimization

Calculate an orthonormal basis

Perform principal component analysis

pcaMethods

Deprecated methods for pcaMethods

Class representation of the NLPCA neural net

Class for representing a PCA result

Plot diagnostics (screeplot)

Plot many side by side scores XOR loadings plots

Probabilistic PCA

Predict values from PCA.

Pre-process a matrix for PCA

Residuals values from a PCA model.

Replicate and tile an array.

PCA implementation based on robustSvd

Alternating L1 Singular Value Decomposition

Get the standard deviations of the scores (indicates their relevance)

Check if scaling was part of the PCA model

Get the scales (e.g. standard deviations) of the original variables

Get scores from a pcaRes object

Get scores from a pcaRes object

Print/Show for pcaRes

Print a nniRes model

Hotelling's T^2 Ellipse

Side by side scores and loadings plot

Sort the features of NLPCA object

Summary of PCA model

SVDimpute algorithm

Perform principal component analysis using singular value decomposition

Temporary fix for missing values

Tranform the vectors of weights to matrix structure

Tranform the vectors of weights to matrix structure

Get a matrix with indicating the elements that were missing in the input data. Convenient for estimating imputation performance.

Create an object that holds the weights for nlpcaNet. Holds and sets weights in using an environment object.