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 of the pseudorandom generator used in the importance sampling algorithm for likelihood approximation in case of discrete responses.
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.
logical. Should standard errors be computed and returned or not?
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.