# gcmr.options

##### Setting Options for Fitting Gaussian Copula Marginal Regression Models

Sets options that affect the fitting of Gaussian copula marginal regression models.

- Keywords
- regression, nonlinear

##### Usage

```
gcmr.options(seed = round(runif(1, 1, 1e+05)), nrep = c(100, 1000),
no.se = FALSE, method = c("BFGS", "Nelder-Mead", "CG"), ...)
```

##### Arguments

- seed
seed of the pseudorandom generator used in the importance sampling algorithm for likelihood approximation in case of discrete responses.

- nrep
Monte Carlo size of the importance sampling algorithm for likelihood approximation in case of discrete responses.

`nrep`

can be a vector so that the model is fitted with a sequence of different Monte Carlo sizes. In this case, the starting values for optimization of the likelihood are taken from the previous fitting. A reasonable strategy is to fit the model with a small Monte Carlo size to obtain sensible starting values and then refit with a larger Monte Carlo size. The default value is`100`

for the first optimization and`1000`

for the second and definitive optimization.- no.se
logical. Should standard errors be computed and returned or not?

- method
a character string specifying the method argument passed to

`optim`

. The default optimization routine is the quasi-Newton algorithm`BFGS`

. See`optim`

for details.- ...
arguments passed to

`optim`

.

##### Value

A list containing the options.

##### References

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. *Electronic Journal of Statistics* **6**, 1517--1549. http://projecteuclid.org/euclid.ejs/1346421603.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. *Journal of Statistical Software*, **77**(8), 1--26. 10.18637/jss.v077.i08.

##### See Also

*Documentation reproduced from package gcmr, version 1.0.2, License: GPL (>= 2)*