summary.sdlrm: Summarizing a Modified Skew Discrete Laplace Regression Fit

In sdlrm: Modified Skew Discrete Laplace Regression for Integer-Valued and Paired Discrete Data

Summarizing a Modified Skew Discrete Laplace Regression Fit

Description

summary method for class "sdlrm".

Usage

## S3 method for class 'sdlrm'
summary(object, ...)

## S3 method for class 'summary.sdlrm'
print(x, digits = getOption("digits"), ...)

Arguments

object	an object of class "sdlrm", a result of a call to sdlrm.
...	further arguments passed to or from other methods.
x	an object of class "summary.sdlrm", a result of a call to summary.sdlrm.
digits	a non-null value for digits specifies the minimum number of significant digits to be printed in values.

Value

The function summary.sdlrm returns an object of class "summary.sdlrm", which consists of a list with the following components:

call

the original function call, given in object.

mean

summary statistics for the mean regression structure.

dispersion

summary statistics for the dispersion regression structure.

xi

the specified mode for the model.

phi.link

the link function used for the dispersion parameter model.

residuals

the randomized quantile residuals.

pR2

the pseudo-R2 for integer-valued regression models, as introduced by Medeiros and Bourguignon (2025).

logLik

log-likelihood value of the fitted model.

AIC, BIC

Akaike and Bayesian information criteria.

Author(s)

Francisco F. de Queiroz <[e-mail]>

Rodrigo M. R. de Medeiros <[e-mail]>

References

Medeiros, R. M. R., and Bourguignon, M. (2025). Modified skew discrete Laplace regression models for integer valued data with applications to paired samples. Manuscript submitted for publication.

Examples

# Data set: pss (for description run ?pss)
barplot(table(pss$difference), xlab = "PSS index difference", ylab = "Frequency")
boxplot(pss$difference ~ pss$group, xlab = "Group", ylab = "PSS index difference")

# Fit with a model only for the mean (mode = 1)
fit0 <- sdlrm(difference ~ group, data = pss, xi = 1)
summary(fit0)

# Fit a double model (mean and dispersion)
fit <- sdlrm(difference ~ group | group, data = pss, xi = 1)
summary(fit)

sdlrm documentation built on April 12, 2025, 1:15 a.m.