lqmControl: Control parameters for lqm estimation
In gforge/lqmm_gforge: Linear Quantile Mixed Models

Description Usage Arguments Details Value Author(s) References See Also

A list of parameters for controlling the fitting process.

lqmControl(
  method = "gs1",
  loop_tol_ll = 1e-05,
  loop_tol_theta = 0.001,
  check_theta = FALSE,
  loop_step = NULL,
  beta = 0.5,
  gamma = 1.25,
  reset_step = FALSE,
  loop_max_iter = 1000,
  smooth = FALSE,
  omicron = 0.001,
  verbose = FALSE
)

`method`	character vector that specifies which code to use for carrying out the gradient search algorithm: "gs1" (default) based on C code and "gs2" based on R code. Method "gs3" uses a smoothed loss function. See details.
`loop_tol_ll`	tolerance expressed as relative change of the log-likelihood.
`loop_tol_theta`	tolerance expressed as relative change of the estimates.
`check_theta`	logical flag. If `TRUE` the algorithm performs a check on the change in the estimates in addition to the likelihood.
`loop_step`	step size (default standard deviation of response).
`beta`	decreasing step factor for line search (0,1).
`gamma`	nondecreasing step factor for line search (>= 1).
`reset_step`	logical flag. If `TRUE` the step size is re-setted to the initial value at each iteration.
`loop_max_iter`	maximum number of iterations.
`smooth`	logical flag. If `TRUE` the standard loss function is replaced with a smooth approximation.
`omicron`	small constant for smoothing the loss function when using `smooth = TRUE`. See details.
`verbose`	logical flag.

The methods "gs1" and "gs2" implement the same algorithm (Bottai et al, 2015). The former is based on C code, the latter on R code. While the C code is faster, the R code seems to be more efficient in handling large datasets. For method "gs2", it is possible to replace the classical non-differentiable loss function with a smooth version (Chen, 2007).

a list of control parameters.

Marco Geraci

Bottai M, Orsini N, Geraci M (2015). A Gradient Search Maximization Algorithm for the Asymmetric Laplace Likelihood, Journal of Statistical Computation and Simulation, 85(10), 1919-1925.

Chen C (2007). A finite smoothing algorithm for quantile regression. Journal of Computational and Graphical Statistics, 16(1), 136-164.

lqm

gforge/lqmm_gforge documentation built on Dec. 20, 2021, 10:42 a.m.