|
The function contains the actual implementation of the BPCA component estimation. It performs one step of the BPCA EM algorithm. It is called 'maxStep' times from within the main loop in BPCAestimate.
● Data Source:
BioConductor
● Keywords:
● Alias: BPCA_dostep
●
0 images
|
|
Model initialization for Bayesian PCA. This function is NOT inteded to be run separately!
● Data Source:
BioConductor
● Keywords:
● Alias: BPCA_initmodel
●
0 images
|
Distance to the model of X-space.
● Data Source:
BioConductor
● Keywords:
● Alias: DModX, DModX,pcaRes-method
●
1 images
|
Q2
(Package: pcaMethods) :
Cross-validation for PCA
Internal cross-validation can be used for estimating the level of structure in a data set and to optimise the choice of number of principal components.
● Data Source:
BioConductor
● Keywords: multivariate
● Alias: Q2
●
2 images
|
|
Flexible calculation of R2 goodness of fit.
● Data Source:
BioConductor
● Keywords:
● Alias: R2VX, R2VX,pcaRes-method
●
0 images
|
R2cum,pcaRes-method
(Package: pcaMethods) :
Cumulative R2 is the total ratio of variance that is being
Cumulative R2 is the total ratio of variance that is being explained by the model
● Data Source:
BioConductor
● Keywords:
● Alias: R2cum, R2cum,pcaRes-method
●
0 images
|
RnipalsPca
(Package: pcaMethods) :
NIPALS PCA implemented in R
PCA by non-linear iterative partial least squares
● Data Source:
BioConductor
● Keywords: multivariate
● Alias: RnipalsPca
●
0 images
|
asExprSet
(Package: pcaMethods) :
Convert pcaRes object to an expression set
This function can be used to conveniently replace the expression matrix in an ExpressionSet with the completed data from a pcaRes object.
● Data Source:
BioConductor
● Keywords: multivariate
● Alias: asExprSet
●
0 images
|
biplot-methods
(Package: pcaMethods) :
Plot a overlaid scores and loadings plot
Visualize two-components simultaneously
● Data Source:
BioConductor
● Keywords: multivariate
● Alias: biplot,pcaRes-method, biplot-methods, biplot.pcaRes
●
1 images
|
bpca
(Package: pcaMethods) :
Bayesian PCA missing value estimation
Implements a Bayesian PCA missing value estimator. The script is a port of the Matlab version provided by Shigeyuki OBA. See also http://ishiilab.jp/member/oba/tools/BPCAFill.html. BPCA combines an EM approach for PCA with a Bayesian model. In standard PCA data far from the training set but close to the principal subspace may have the same reconstruction error. BPCA defines a likelihood function such that the likelihood for data far from the training set is much lower, even if they are close to the principal subspace.
● Data Source:
BioConductor
● Keywords: multivariate
● Alias: bpca
●
1 images
|
providing 
.