gaussian.marg {gcmr} | R Documentation |
Marginals in Gaussian Copula Marginal Regression Models
Description
These functions set the marginals in Gaussian copula marginal regression models.
Usage
beta.marg(link = "logit")
binomial.marg(link = "logit")
Gamma.marg(link = "inverse")
gaussian.marg(link = "identity")
negbin.marg(link = "log")
poisson.marg(link = "log")
weibull.marg(link = "log")
Arguments
link |
a specification for the model link function. See |
Details
Beta marginals specified by beta.marg
are parametrized in terms of mean and dispersion as in betareg
. See Cribari-Neto and Zeileis (2010) and Ferrari and Cribari-Neto (2004).
For binomial marginals specified by binomial.marg
, the response is specified as a factor when the first level denotes failure and all others success or as a two-column matrix with the columns giving the numbers of successes and failures.
Negative binomial marginals implemented in negbin.marg
are parametrized such that .
For back-compatibility with previous versions of the gcmr
package, short names for the marginals bn.marg
, gs.marg
, nb.marg
, and ps.marg
remain valid as an alternative to (preferred) longer versions binomial.marg
, gaussian.marg
, negbin.marg
, and poisson.marg
.
Value
An object of class marginal.gcmr
representing the marginal component.
Author(s)
Guido Masarotto and Cristiano Varin.
References
Cribari-Neto, F. and Zeileis, A. (2010). Beta regression in R. Journal of Statistical Software 34, 1–24.
Ferrari, S.L.P. and Cribari-Neto, F. (2004). Beta regression for modeling rates and proportions. Journal of Applied Statistics 31 (7), 799–815.
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.