image.sgdgmf: Heatmap of a GMF model

In sgdGMF: Estimation of Generalized Matrix Factorization Models via Stochastic Gradient Descent

Heatmap of a GMF model

Description

Plots a heatmap of either the data, the fitted values, or the residual values of a GMF model allowing for different types of transformations and normalizations. Moreover, it also permits to plot the latent score and loading matrices.

Usage

## S3 method for class 'sgdgmf'
image(
  x,
  ...,
  type = c("data", "response", "link", "scores", "loadings", "deviance", "pearson",
    "working"),
  resid = FALSE,
  symmetric = FALSE,
  transpose = FALSE,
  limits = NULL,
  palette = NULL
)

Arguments

x	an object of class sgdgmf
...	further arguments passed to or from other methods
type	the type of data/predictions/residuals which should be returned
resid	if TRUE, plots the residual values
symmetric	if TRUE, symmetrizes the color limits
transpose	if TRUE, transposes the matrix before plotting it
limits	the color limits which should be used
palette	the color-palette which should be used

Value

A ggplot object showing the selected heatmap.

Examples

# Load the sgdGMF package
library(sgdGMF)

# Generate data from a Poisson model
data = sim.gmf.data(n = 100, m = 20, ncomp = 5, family = poisson())

 # Fit a GMF model
 gmf = sgdgmf.fit(data$Y, ncomp = 3, family = poisson())

# Get the heatmap of a GMF model
image(gmf, type = "data") # original data
image(gmf, type = "response") # fitted values in response scale
image(gmf, type = "scores") # estimated score matrix
image(gmf, type = "loadings") # estimated loading matrix
image(gmf, type = "deviance", resid = TRUE) # deviance residual matrix

sgdGMF documentation built on April 3, 2025, 7:37 p.m.