glmx: glmx.control – R documentation

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glmx

glmx.control

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.