# betareg.control

##### Control Parameters for Beta Regression

Various parameters that control fitting of beta regression models
using `betareg`

.

- Keywords
- regression

##### Usage

```
betareg.control(phi = TRUE, method = "BFGS", maxit = 5000,
hessian = FALSE, trace = FALSE, start = NULL,
fsmaxit = 200, fstol = 1e-8, …)
```

##### Arguments

- phi
logical indicating whether the precision parameter phi should be treated as a full model parameter (

`TRUE`

, default) or as a nuisance parameter.- method
characters string specifying the

`method`

argument passed to`optim`

.- maxit
integer specifying the

`maxit`

argument (maximal number of iterations) passed to`optim`

.- trace
logical or integer controlling whether tracing information on the progress of the optimization should be produced (passed to

`optim`

).- hessian
logical. Should the numerical Hessian matrix from the

`optim`

output be used for estimation of the covariance matrix? By default the analytical solution is employed. For details see below.- start
an optional vector with starting values for all parameters (including phi).

- fsmaxit
integer specifying maximal number of additional (quasi) Fisher scoring iterations. For details see below.

- fstol
numeric tolerance for convergence in (quasi) Fisher scoring. For details see below.

- …
arguments passed to

`optim`

.

##### Details

All parameters in `betareg`

are estimated by maximum likelihood
using `optim`

with control options set in `betareg.control`

.
Most arguments are passed on directly to `optim`

, and `start`

controls
how `optim`

is called.

After the `optim`

maximization, an additional (quasi) Fisher scoring
can be perfomed to further enhance the result or to perform additional bias reduction.
If `fsmaxit`

is greater than zero, this additional optimization is
performed and it converges if the threshold `fstol`

is attained
for the cross-product of the step size.

Starting values can be supplied via `start`

or estimated by
`lm.wfit`

, using the link-transformed response.
Covariances are in general derived analytically. Only if `type = "ML"`

and
`hessian = TRUE`

, they are determined numerically using the Hessian matrix
returned by `optim`

. In the latter case no Fisher scoring iterations are
performed.

The main parameters of interest are the coefficients in the linear predictor of the
model and the additional precision parameter phi which can either
be treated as a full model parameter (default) or as a nuisance parameter. In the latter case
the estimation does not change, only the reported information in output from `print`

,
`summary`

, or `coef`

(among others) will be different. See also examples.

##### Value

A list with the arguments specified.

##### See Also

##### Examples

```
# NOT RUN {
options(digits = 4)
data("GasolineYield", package = "betareg")
## regression with phi as full model parameter
gy1 <- betareg(yield ~ batch + temp, data = GasolineYield)
gy1
## regression with phi as nuisance parameter
gy2 <- betareg(yield ~ batch + temp, data = GasolineYield, phi = FALSE)
gy2
## compare reported output
coef(gy1)
coef(gy2)
summary(gy1)
summary(gy2)
# }
```

*Documentation reproduced from package betareg, version 3.1-3, License: GPL-2 | GPL-3*