R: Non-Negative Matrix Factorization with Kernel Covariates

# Non-Negative Matrix Factorization with Kernel Covariates

## Documentation for package ‘nmfkc’ version 0.6.7

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

coef.nmf | Extract coefficients from NMF models
coef.nmf.sem | Extract coefficients from NMF models
fitted.nmf | Extract fitted values from NMF models
fitted.nmf.sem | Extract fitted values from NMF models
fitted.nmfae | Extract fitted values from NMF models
nmf.sem | NMF-SEM Main Estimation Algorithm
nmf.sem.cv | Cross-Validation for NMF-SEM
nmf.sem.DOT | Generate a Graphviz DOT Diagram for an NMF-SEM Model
nmf.sem.inference | Statistical inference for the exogenous parameter matrix C2
nmf.sem.split | Heuristic Variable Splitting for NMF-SEM
nmfae | Three-Layer Non-negative Matrix Factorization (NMF-AE)
nmfae.cv | Sample-wise k-fold Cross-Validation for nmfae
nmfae.DOT | DOT graph visualization for nmfae objects
nmfae.ecv | Element-wise Cross-Validation for nmfae (Wold's CV)
nmfae.heatmap | Heatmap visualization of nmfae factor matrices
nmfae.inference | Statistical Inference for NMF-AE Parameter Matrix
nmfae.kernel.beta.cv | Optimize kernel beta for nmfae by cross-validation
nmfae.rename | Rename decoder and encoder bases
nmfkc | Optimize NMF with kernel covariates (Full Support for Missing Values)
nmfkc.ar | Construct observation and covariate matrices for a vector autoregressive model
nmfkc.ar.degree.cv | Optimize lag order for the autoregressive model
nmfkc.ar.DOT | Generate a Graphviz DOT Diagram for NMF-AR / NMF-VAR Models
nmfkc.ar.predict | Forecast future values for NMF-VAR model
nmfkc.ar.stationarity | Check stationarity of an NMF-VAR model
nmfkc.class | Create a class (one-hot) matrix from a categorical vector
nmfkc.criterion | Compute model selection criteria for a fitted nmfkc model
nmfkc.cv | Perform k-fold cross-validation for NMF with kernel covariates
nmfkc.denormalize | Denormalize a matrix from [0,1] back to its original scale
nmfkc.DOT | Generate Graphviz DOT Scripts for NMF or NMF-with-Covariates Models
nmfkc.ecv | Perform Element-wise Cross-Validation (Wold's CV)
nmfkc.inference | Statistical inference for the parameter matrix C (Theta)
nmfkc.kernel | Create a kernel matrix from covariates
nmfkc.kernel.beta.cv | Optimize beta of the Gaussian kernel function by cross-validation
nmfkc.kernel.beta.nearest.med | Estimate Gaussian/RBF kernel parameter beta from covariates (supports landmarks)
nmfkc.kernel.gaussian | Create a Gaussian kernel matrix from covariates
nmfkc.normalize | Normalize a matrix to the range [0,1]
nmfkc.rank | Rank selection diagnostics with graphical output
nmfkc.residual.plot | Plot Diagnostics: Original, Fitted, and Residual Matrices as Heatmaps
nmfre | Non-negative Matrix Factorization with Random Effects
nmfre.dfU.scan | Scan dfU cap rates for NMF-RE
nmfre.inference | Statistical inference for the coefficient matrix C from NMF-RE
plot.nmf.sem | Plot convergence diagnostics for NMF models
plot.nmfae | 'plot.nmfae' displays the convergence trajectory of the objective function across iterations. The title shows the achieved R^2.
plot.nmfae.cv | Plot method for nmfae.cv objects
plot.nmfae.ecv | Plot method for nmfae.ecv objects
plot.nmfae.kernel.beta.cv | Plot method for nmfae.kernel.beta.cv objects
plot.nmfkc | Plot method for objects of class 'nmfkc'
plot.nmfkc.DOT | Plot method for nmfkc.DOT objects
plot.nmfre | Plot convergence diagnostics for NMF models
plot.predict.nmfae | Plot method for predict.nmfae objects
predict.nmfae | Predict method for nmfae objects
predict.nmfkc | Prediction method for objects of class 'nmfkc'
print.nmf.inference | Print method for NMF inference objects
print.summary.nmfae | Print method for summary.nmfae objects
print.summary.nmfae.inference | Print method for summary.nmfae.inference objects
print.summary.nmfkc | Print method for 'summary.nmfkc' objects
print.summary.nmfkc.inference | Print method for summary.nmfkc.inference objects
residuals.nmf | Extract residuals from NMF models
residuals.nmf.sem | Extract residuals from NMF models
residuals.nmfae | Extract residuals from NMF models
residuals.nmfre | Extract residuals from NMF models
summary.nmf.sem | Summary method for nmf.sem objects
summary.nmfae | Summary method for nmfae objects
summary.nmfae.inference | Summary method for nmfae.inference objects
summary.nmfkc | Summary method for objects of class 'nmfkc'
summary.nmfkc.inference | Summary method for nmfkc.inference objects
summary.nmfre | Summary method for objects of class 'nmfre'