galamm_control {galamm}

Control values for galamm fit

Description

This function can be called for controling the optimization procedure used when fitting GALAMMs using galamm.

Usage

galamm_control(
  optim_control = list(),
  method = c("L-BFGS-B", "Nelder-Mead"),
  maxit_conditional_modes = 10,
  pirls_tol_abs = 0.01,
  reduced_hessian = FALSE
)

Arguments

optim_control

List containing optimization parameters. If method = "L-BFGS-B" it is passed on to stats::optim's control argument and if method = "Nelder-Mead", it is passed on to lme4::Nelder_Mead's control argument. If not otherwise specified, and method = "L-BFGS-B", the following arguments are set to non-default values: fnscale = -1 and lmm = 20.

method

Character string defining the algorithm to be used for maximizing the marginal log-likelihood. The default is "L-BFGS-B", which uses the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm with box constrained as implemented in stats::optim. The other options is "Nelder-Mead", which calls the Nelder-Mead algorithm with box constraints implemented in lme4::Nelder_Mead. The argument is case sensitive.

maxit_conditional_modes

Maximum number of iterations in penalized iteratively reweighted least squares algorithm. Ignored if family = "gaussian" for all observations, since then a single step gives the exact answer.

pirls_tol_abs

Absolute convergence criterion for penalized iteratively reweighted least squares algorithm. Defaults to 0.01, which means that when the reduction in marginal likelihood between two iterations is below 0.01, the iterations stop.

reduced_hessian

Logical value. Defaults to TRUE, which means that the full Hessian matrix at the maximum marginal likelihood solution is computed. If FALSE, a reduced Hessian matrix with second order partial derivatives with respect to fixed regression coefficients and factor loadings. The latter can help is the full Hessian is not positive definite.

Value

Object of class galamm_control, which typically will be provided as an argument to galamm.

References

Bates DM, Mächler M, Bolker B, Walker S (2015). “Fitting Linear Mixed-Effects Models Using Lme4.” Journal of Statistical Software, 67(1), 1–48. ISSN 1548-7660, doi:10.18637/jss.v067.i01.

BROYDEN CG (1970). “The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations.” IMA Journal of Applied Mathematics, 6(1), 76–90. ISSN 0272-4960, doi:10.1093/imamat/6.1.76.

Byrd RH, Lu P, Nocedal J, Zhu C (1995). “A Limited Memory Algorithm for Bound Constrained Optimization.” SIAM Journal on Scientific Computing, 16(5), 1190–1208. ISSN 1064-8275, doi:10.1137/0916069.

Fletcher R (1970). “A New Approach to Variable Metric Algorithms.” The Computer Journal, 13(3), 317–322. ISSN 0010-4620, doi:10.1093/comjnl/13.3.317.

Goldfarb D (1970). “A Family of Variable-Metric Methods Derived by Variational Means.” Mathematics of Computation, 24(109), 23–26. ISSN 0025-5718, 1088-6842, doi:10.1090/S0025-5718-1970-0258249-6.

Nelder JA, Mead R (1965). “A Simplex Method for Function Minimization.” The Computer Journal, 7(4), 308–313. ISSN 0010-4620, doi:10.1093/comjnl/7.4.308.

Shanno DF (1970). “Conditioning of Quasi-Newton Methods for Function Minimization.” Mathematics of Computation, 24(111), 647–656. ISSN 0025-5718, 1088-6842, doi:10.1090/S0025-5718-1970-0274029-X.

See Also

galamm()

Other optimization functions: extract_optim_parameters.galamm()

Examples

# Define control object with quite a high degree of verbosity (trace = 6)
# and using the last 20 BFGS updates to estimate the Hessian in L-BFGS-B.
control <- galamm_control(optim_control = list(trace = 6, lmm = 20))