PLregcontrol: Auxiliary for Controlling PL Fitting

In ffqueiroz/PLreg: Power Logit Regression for Modeling Bounded Data

Auxiliary for Controlling PL Fitting

Description

Parameters that control fitting of power logit regression models using PLreg.

Usage

PLreg.control(
  lambda = NULL,
  method = "BFGS",
  maxit = 2000,
  trace = FALSE,
  start = NULL,
  ...
)

Arguments

lambda	numeric indicating the value of the skewness parameter lambda (if NULL, lambda will be estimated).
method	character specifying the method argument passed to optim.
maxit, trace, ...	arguments passed to optim
start	an optional vector with starting values for median and dispersion submodels (starting value for lambda must not be included).

Details

The PLreg.control controls the fitting process of power logit models. Almost all the arguments are passed on directly to optim, which is used to estimate the parameters. Starting values for median and dispersion submodels may be supplied via start. If the estimation process is to be performed with a fixed skewness parameter, a value must be specified in lambda. If lambda = 0, a log-log regression model will be estimated.

Value

A list with components named as the arguments.

See Also

PLreg

Examples

data("PeruVotes")

fitPL <- PLreg(votes ~ HDI | HDI, data = PeruVotes,
              family = "TF", zeta = 5, control = PLreg.control(lambda = 1))
summary(fitPL)

ffqueiroz/PLreg documentation built on March 1, 2023, 2:27 a.m.