A list of parameters for controlling the fitting process.
nlControl(tol_ll = 1e-05, tol_theta = 0.001, check_theta = FALSE,
step = NULL, beta = 0.5, gamma = 1.25, reset_step = FALSE,
maxit = 1000, smooth = FALSE, omicron = 0.001, verbose = FALSE)a list of control parameters.
tolerance expressed as relative change of the objective function.
tolerance expressed as relative change of the estimates.
logical flag. If TRUE the algorithm performs a check on the change in the estimates in addition to the likelihood.
step size (default standard deviation of response).
decreasing step factor for line search (0,1).
nondecreasing step factor for line search (>= 1).
logical flag. If TRUE the step size is re-setted to the initial value at each iteration.
maximum number of iterations.
logical flag. If TRUE the standard loss function is replaced with a smooth approximation.
small constant for smoothing the loss function when using smooth = TRUE. See details.
logical flag.
Marco Geraci
The optimization algorithm is along the lines of the gradient search algorithm (Bottai et al, 2015). If smooth = TRUE, the classical non-differentiable loss function is replaced with a smooth version (Chen and Wei, 2005).
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, Wei Y (2005). Computational issues for quantile regression. Sankhya: The Indian Journal of Statistics, 67(2), 399-417.
nlrq1