lqmControl() R function from [lqmm]

Control parameters for lqm estimation

A list of parameters for controlling the fitting process.


lqmControl(method = "gs1", loop_tol_ll = 1e-5, loop_tol_theta = 1e-3,
        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)

Arguments

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.

Returns

a list of control parameters.

Details

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).

References

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.

Author(s)

Marco Geraci

lqmControl function