unitquantreg.control: Control parameters for unit quantile regression

In unitquantreg: Parametric Quantile Regression Models for Bounded Data

Control parameters for unit quantile regression

Description

Auxiliary function that control fitting of unit quantile regression models using unitquantreg.

Usage

unitquantreg.control(
  method = "BFGS",
  hessian = FALSE,
  gradient = TRUE,
  maxit = 5000,
  factr = 1e+07,
  reltol = sqrt(.Machine$double.eps),
  trace = 0L,
  starttests = FALSE,
  dowarn = FALSE,
  ...
)

Arguments

method	string. Specify the method argument passed to optimx.
hessian	logical. Should use the numerically Hessian matrix to compute variance-covariance? Default is FALSE, i.e., use the analytic Hessian.
gradient	logical. Should use the analytic gradient? Default is TRUE.
maxit	integer. Specify the maximal number of iterations passed to optimx.
factr	numeric.Controls the convergence of the "L-BFGS-B" method.
reltol	numeric. Relative convergence tolerance passed to optimx.
trace	non-negative integer. If positive, tracing information on the progress of the optimization is produced.
starttests	logical. Should optimx run tests of the functions and parameters? Default is FALSE.
dowarn	logical. Show warnings generated by optimx? Default is FALSE.
...	arguments passed to optimx.

Details

The control argument of unitquantreg uses the arguments of unitquantreg.control. In particular, the parameters in unitquantreg are estimated by maximum likelihood using the optimx, which is a general-purpose optimization wrapper function that calls other R tools for optimization, including the existing optim function. The main advantage of optimx is to unify the tools allowing a number of different optimization methods and provide sanity checks.

Value

A list with components named as the arguments.

Author(s)

André F. B. Menezes

References

Nash, J. C. and Varadhan, R. (2011). Unifying Optimization Algorithms to Aid Software System Users: optimx for R., Journal of Statistical Software, 43(9), 1–14.

See Also

optimx for more details about control parameters and unitquantreg.fit the fitting procedure used by unitquantreg.

Examples

data(sim_bounded, package = "unitquantreg")
sim_bounded_curr <- sim_bounded[sim_bounded$family == "uweibull", ]

# Fitting using the analytical gradient
fit_gradient <- unitquantreg(formula = y1 ~ x,
                             data = sim_bounded_curr, tau = 0.5,
                             family = "uweibull",
                             control = unitquantreg.control(gradient = TRUE,
                                                            trace = 1))

# Fitting without using the analytical gradient
fit_nogradient <- unitquantreg(formula = y1 ~ x,
                             data = sim_bounded_curr, tau = 0.5,
                             family = "uweibull",
                             control = unitquantreg.control(gradient = FALSE,
                                                            trace = 1))
# Compare estimated coefficients
cbind(gradient = coef(fit_gradient), no_gradient = coef(fit_nogradient))

unitquantreg documentation built on Sept. 8, 2023, 5:40 p.m.