glmx.control: Control Parameters for GLMs with Extra Parameters

In glmx: Generalized Linear Models Extended

Control Parameters for GLMs with Extra Parameters

Description

Various parameters that control fitting of generalized linear models with extra parameters using glmx.

Usage

glmx.control(profile = TRUE, nuisance = FALSE,
  start = NULL, xstart = NULL, hessian = TRUE, method = "BFGS", 
  epsilon = 1e-8, maxit = c(500, 25), trace = FALSE,
  reltol = .Machine$double.eps^(1/1.2), ...)

Arguments

profile	logical. Should the extra parameters be optimized via profile likelihood (or via the full likelihood of all parameters)?
nuisance	logical. Should the extra parameters be treated as nuisance parameters (i.e., suppressed in subsequent output)?
start	an optional vector with starting values for the GLM coefficients.
xstart	an optional vector with starting values for the extra parameter(s). Must be supplied if there is more than one extra parameter.
hessian	logical or character. Should the hessian be computed to estimate the covariance matrix? If character, hessian can be either "none", "optim" or "numDeriv". The default is the hessian from optim but alternatively hessian from the numDeriv package can be used.
method	characters string specifying the method argument passed to optim.
epsilon	numeric convergance tolerance passed to glm.control.
maxit	integer specifying the maxit argument (maximal number of iterations) passed to optim and glm.control. Can also be a vector of length 2.
trace	logical or integer controlling whether tracing information on the progress of the optimization should be produced (passed to optim, and glm.control). Can also be a vector of length 2.
reltol, ...	arguments passed to optim.

Details

All parameters in glmx are estimated by maximum likelihood using optim with control options set in glmx.control. Either the parameters can be found by only optimizing over the extra parameters (and then using glm.fit to estimate the GLM coefficients), or alternatively all parameters can be optimized simultaneously. Covariances are derived numerically using the Hessian matrix returned by optim.

Value

A list with the arguments specified.

See Also

glmx

glmx documentation built on March 31, 2023, 3:10 p.m.