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

## Description

Set control parameters for cumulative link models

## Usage

 ```1 2 3 4 5``` ```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")) ```

## Arguments

 `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`.

## Value

a list of control parameters.

## Author(s)

Rune Haubo B Christensen

`clm`