nlmmControl: Control parameters for nlmm estimation

In nlmm: Generalized Laplace Mixed-Effects Models

Control parameters for nlmm estimation

Description

A list of parameters for controlling the fitting process.

Usage

nlmmControl(method = "Nelder-Mead", nK = 8, multistart = TRUE,
	grid = c(0.001, 0.5, 0.999), alpha = c(0.5, 0.5), alpha.index = 9,
	lme = TRUE, lmeMethod = "REML", lmeOpt = "nlminb", verbose = FALSE)

Arguments

method	character vector that specifies the optimization algorithm to fit a generalized Laplace mixed-effects model. The default is "Nelder-Mead".
nK	number of knots for each of the two quadratures.
multistart	logical flag. If TRUE (default), the algorithm is run with multiple starting values for the parameter alpha. See also grid.
grid	a vector of values for multi-start optimization. It can be used in conjunction with constrained estimation.
alpha	vector of length 2 with starting values between 0 and 1 for the parameter alpha (ignored if multistart is TRUE) or values at which alpha is constrained if alpha.index is one of 0, 1, or 2. The first element is for the shape parameter of the random effects, the second for the error term. See Geraci and Farcomeni (2020).
alpha.index	the estimation with parameter alpha is unconstrained if alpha.index is equal to 9 (default). If equal to 0, both shape parameters (random effects and error term) are constrained during estimation and set equal to alpha. If equal to 1, the first shape parameter (random effects) is constrained during estimation and set equal to the first element of alpha. If equal to 2, the second shape parameter (error term) is constrained during estimation and set equal to the second element of alpha.
lme	logical flag. Should lme be used to get starting values? If FALSE, lm is used instead.
lmeMethod	fitting method for lme when obtaining starting values. If "REML" the model is fit by maximizing the restricted log-likelihood. If "ML" the log-likelihood is maximized.
lmeOpt	optimization algorithm for lme. Either either nlminb (the default) or optim. This is passed to argument opt in lmeControl.
verbose	logical flag. If TRUE, information about the fitting process is printed out.

Details

The estimation algorithm for fitting generalized Laplace mixed-effects (GLME) models is described in Geraci and Farcomeni (2020). For unconstrained estimation, it is recommended to leave the default arguments in nlmmControl unchanged.

The integrated log-likelihood is maximized with either optim, in which case method has to be one of optim's options ("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN", "Brent"), or nlminb, in which case one must use method = "nlminb".

Since the parameter alpha is bidimensional, care should be taken when increasing the number of quadrature knots nK since the total number of quadrature points is given by 2^{nK}. For the same reason, care should be taken when providing the grid values for multi-start optimization since the total number of starting points will be s^{2}, where s = length(grid).

If alpha.index is 1 (or 2), the first (or second) element of the alpha parameter is constrained during estimation and set equal to the corresponding value of alpha. The element of the alpha parameter that is unconstrained is initialized with the corresponding element of alpha (if multistart is FALSE) or with values in grid (if multistart is TRUE).

If alpha.index is 0, both elements of the alpha parameter are fixed and set equal to alpha. In this case, the argument multistart is ignored. If alpha is c(0,0), the corresponding model is Normal-Normal and lme is used for fitting (only via maximum likelihood). Note that in this case, lmeOpt can still be used.

Value

a list of control parameters.

Author(s)

Marco Geraci

References

Geraci, M. and Farcomeni A. (2020). A family of linear mixed-effects models using the generalized Laplace distribution. Statistical Methods in Medical Research, 29(9), 2665-2682.

See Also

nlmm

nlmm documentation built on Nov. 24, 2023, 5:11 p.m.