gcmr.options

From gcmr v1.0.2 by Cristiano Varin

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

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

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.

A list containing the options.

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.

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