sdlrm: Modified Skew Discrete Laplace Regression for Integer-Valued...

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

Modified Skew Discrete Laplace Regression for Integer-Valued Data

Description

Fit of the modified skew discrete Laplace (SDL) regression model via maximum likelihood for a parameterization of this distribution that is indexed by the mean, a dispersion parameter, and the mode (xi).

Usage

sdlrm(
  formula,
  data,
  subset,
  na.action,
  phi.link = "log",
  xi = 0,
  control = sdl_control(...),
  ...
)

## S3 method for class 'sdlrm'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

formula	a symbolic description of the model, of type y ~ x for covariates in mean only, or y ~ x | z to enter covariates in the dispersion parameter.
data	an optional data frame containing the variables in the formula. By default the variables are taken from environment(formula).
subset	an optional vector specifying a subset of observations to be used in the fitting process. (See additional details about how this argument interacts with data-dependent bases in the ‘Details’ section of the model.frame documentation.)
na.action	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The ‘factory-fresh’ default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.
phi.link	character specification of the link function for the dispersion parameter. The links "log" (default) "sqrt" and "identity" can be used.
xi	the mode of the distribution, an integer value.
control	a list of control arguments specified via sdl_control.
...	arguments passed to optim via sdl_control.
x	a fitted model object of class "sdlrm".
digits	a non-null value for digits specifies the minimum number of significant digits to be printed in values.

Value

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

coefficients

a list containing the elements "mean" and "dispersion" that consist of the estimates of the coefficients associated with the mean and the dispersion parameter, respectively.

fitted.values

a vector with the fitted means.

phi

a vector with the fitted dispersion parameters.

phi.link

the link function used for the dispersion parameter model.

xi

the specified mode for the model.

logLik

log-likelihood value of the fitted model.

vcov

asymptotic covariance matrix of the maximum likelihood estimator of the model parameters vector.

nobs

Sample size.

y

the response vector.

x

a list with elements "mean" and "dispersion" containing the model matrices from the respective models.

optim.pars

object returned by optim function in the sdlrm function.

call

the function call.

formula

the formula used to specify the model in sdlrm.

terms

a list with elements "mean", "dispersion" and "full" containing the terms objects for the respective models.

The print() function returns a basic summary of the model fit with the estimated coefficients, the log-likelihood value, the mode specified in the fit, and the Akaike (AIC) and Bayesian (BIC) information criteria.

Author(s)

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.

See Also

summary.sdlrm for more detailed summaries, residuals.sdlrm to extract residuals from the fitted model, predict.sdlrm for predictions, including mean and dispersion fitted values, fitted variances, and fitted quantiles, plot.sdlrm for diagnostic plots. choose_mode for mode estimation via profile likelihood. envelope to create normal probability graphs with simulated envelope. disp_test to test the hypothesis of constant dispersion. Information on additional methods for "sdlrm" objects can be found at sdlrm-methods.

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)
fit0
summary(fit0)

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

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