Package index • nmfkc

All functions

coef(<nmf>) coef(<nmf.sem>): Extract coefficients from NMF models

fitted(<nmf>) fitted(<nmfae>) fitted(<nmf.sem>): Extract fitted values from NMF models

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(): NMF-SEM Main Estimation Algorithm

nmf.sem.split(): Heuristic Variable Splitting for NMF-SEM

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(): Three-Layer Non-negative Matrix Factorization (NMF-AE)

nmfae.rename(): Rename decoder and encoder bases

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(): Construct observation and covariate matrices for a vector autoregressive 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.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.kernel(): Create a kernel matrix from covariates

nmfkc.normalize(): Normalize a matrix to the range \([0,1]\)

nmfkc.rank(): Rank selection diagnostics with graphical output

nmfkc(): Optimize NMF with kernel covariates (Full Support for Missing Values)

nmfkc.residual.plot(): Plot Diagnostics: Original, Fitted, and Residual Matrices as Heatmaps

nmfre.dfU.scan(): Scan dfU cap rates for NMF-RE

nmfre.inference(): Statistical inference for the coefficient matrix C from NMF-RE

nmfre(): Non-negative Matrix Factorization with Random Effects

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(<nmfae>): plot.nmfae displays the convergence trajectory of the objective function across iterations. The title shows the achieved \(R^2\).

plot(<nmfkc.DOT>): Plot method for nmfkc.DOT objects

plot(<nmfkc>): Plot method for objects of class nmfkc

plot(<nmfre>) plot(<nmf.sem>): 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.inference>): Print method for summary.nmfae.inference objects

print(<summary.nmfae>): Print method for summary.nmfae objects

print(<summary.nmfkc.inference>): Print method for summary.nmfkc.inference objects

print(<summary.nmfkc>): Print method for summary.nmfkc objects

residuals(<nmf>) residuals(<nmfae>) residuals(<nmfre>) residuals(<nmf.sem>): Extract residuals from NMF models

summary(<nmf.sem>): Summary method for nmf.sem objects

summary(<nmfae.inference>): Summary method for nmfae.inference objects

summary(<nmfae>): Summary method for nmfae objects

summary(<nmfkc.inference>): Summary method for nmfkc.inference objects

summary(<nmfkc>): Summary method for objects of class nmfkc

summary(<nmfre>): Summary method for objects of class nmfre