screeplot.sgdgmf: Screeplot for the residuals of a GMF model

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

Screeplot for the residuals of a GMF model

Description

Plots the variances of the principal components of the residuals against the number of principal component.

Usage

## S3 method for class 'sgdgmf'
screeplot(
  x,
  ...,
  ncomp = 20,
  type = c("deviance", "pearson", "working", "response", "link"),
  partial = FALSE,
  normalize = FALSE,
  cumulative = FALSE,
  proportion = FALSE
)

Arguments

x	an object of class sgdgmf
...	further arguments passed to or from other methods
ncomp	number of components to be plotted
type	the type of residuals which should be used
partial	if TRUE, plots the eigenvalues of the partial residuals
normalize	if TRUE, plots the eigenvalues of the standardized residuals
cumulative	if TRUE, plots the cumulative sum of the eigenvalues
proportion	if TRUE, plots the fractions of explained variance

Value

A ggplot object showing the residual screeplot of the model.

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 partial residual spectrum of a GMF model
screeplot(gmf) # screeplot of the var-cov matrix of the deviance residuals
screeplot(gmf, partial = TRUE) # screeplot of the partial residuals
screeplot(gmf, cumulative = TRUE) # cumulative screeplot
screeplot(gmf, proportion = TRUE) # proportion of explained residual variance

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