gcmr.options: Setting Options for Fitting Gaussian Copula Marginal...

In gcmr: Gaussian Copula Marginal Regression

Setting Options for Fitting Gaussian Copula Marginal Regression Models

Description

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

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.

Author(s)

Guido Masarotto and Cristiano Varin.

References

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517–1549.

Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26.

See Also

gcmr

gcmr documentation built on July 18, 2022, 9:06 a.m.