# logbin.control: Auxiliary for Controlling logbin Fitting In logbin: Relative Risk Regression Using the Log-Binomial Model

## Description

Auxiliary function for `logbin` fitting. Typically only used internally by `nplbin`, but may be used to construct a `control` argument to that function.

## Usage

 ```1 2``` ```logbin.control(bound.tol = 1e-06, epsilon = 1e-08, maxit = 10000, trace = 0, coeftrace = FALSE) ```

## Arguments

 `bound.tol` positive tolerance specifying the interior of the parameter space. If the fitted model is more than `bound.tol` away from the boundary of the parameter space then it is assumed to be in the interior. This can allow the computational method to terminate early if an interior maximum is found. No early termination is attempted if `bound.tol = Inf`. `epsilon` positive convergence tolerance ε; the estimates are considered to have converged when √{ ∑ (θ_{old} - θ_{new})^2} / √ {∑ θ_{old}^2} < ε, where θ is the vector of parameter estimates. This should be smaller than `bound.tol`. `maxit` integer giving the maximum number of iterations (for a given parameterisation in the case of the CEM algorithm). `trace` number indicating level of output that should be produced. >= 1 gives output for each parameterisation, >= 2 gives output at each iteration. `coeftrace` logical indicating whether the coefficient history should be included as a component of the returned value (for `method = "em"` and `method = "cem"`).

## Details

This is used similarly to `glm.control`. The `control` argument of `logbin` is by default passed to the `control` argument of `nplbin`.

When `trace` is greater than zero, calls to `cat` produce the output. Hence, `options(digits = *)` can be used to increase the precision.

## Value

A list with components named as the arguments.

## Author(s)

Mark W. Donoghoe markdonoghoe@gmail.com

`glm.control`, the equivalent function for `glm` fitting.
`nplbin`, the function used to fit `logbin` models.
 ``` 1 2 3 4 5 6 7 8 9 10``` ```## Variation on example(glm.control) : evts <- c(18,17,15,20,10,20,25,13,12) obs <- rep(30,9) outcome <- gl(3,1,9) treatment <- gl(3,3) oo <- options(digits = 12) logbin.D93X <- logbin(cbind(evts,obs-evts) ~ outcome + treatment, trace = 2, epsilon = 1e-2) options(oo) coef(logbin.D93X) ```