clm.control: Set control parameters for cumulative link models
In ordinal: Regression Models for Ordinal Data

Description Usage Arguments Value Author(s) See Also

Set control parameters for cumulative link models

clm.control(method = c("Newton", "model.frame", "design", "ucminf", "nlminb",
   "optim"), ..., trace = 0L,
   maxIter = 100L, gradTol = 1e-06, maxLineIter = 15L, relTol = 1e-6,
   tol = sqrt(.Machine$double.eps), maxModIter = 5L,
   convergence = c("warn", "silent", "stop", "message"))

`method`	`"Newton"` fits the model by maximum likelihood and `"model.frame"` cause `clm` to return the `model.frame`, `"design"` causes `clm` to return a list of design matrices etc. that can be used with `clm.fit`.
`trace`	numerical, if `> 0` information is printed about and during the optimization process. Defaults to `0`.
`maxIter`	the maximum number of Newton-Raphson iterations. Defaults to `100`.
`gradTol`	the maximum absolute gradient; defaults to `1e-6`.
`maxLineIter`	the maximum number of step halfings allowed if a Newton(-Raphson) step over shoots. Defaults to `10`.
`relTol`	relative convergence tolerence: relative change in the parameter estimates between Newton iterations. Defaults to `1e-6`.
`tol`	numerical tolerence on eigenvalues to determine negative-definiteness of Hessian. If the Hessian of a model fit is negative definite, the fitting algorithm did not converge. If the Hessian is singular, the fitting algorithm did converge albeit not to a unique optimum, so one or more parameters are not uniquely determined even though the log-likelihood value is.
`maxModIter`	the maximum allowable number of consecutive iterations where the Newton step needs to be modified to be a decent direction. Defaults to `5`.
`convergence`	action to take if the fitting algorithm did not converge.
`...`	control arguments parsed on to `ucminf`, `nlminb` or `optim`.