copreg.gamma: Bivariate Copula Regression with Gamma Marginal Distributions
In senhu/mvClaim: Multivariate general insurance claims modelling

Description Usage Arguments Value Examples

Bivariate copula regression models where the marginal distributions are of Gamma GLMs.

1 2	copreg.gamma(f1, f2, copula, data, optim.method = "BFGS", optim.control = list(fnscale = -1, trace = 1))

`f1`	A regression formula for the first marginal GLM.
`f2`	A regression formula for the second marginal GLM.
`copula`	A copula specified from the `copula` package.
`data`	A matrix or data frame of observations. Categorical variables are allowed as covariates.
`optim.method`	Method for the `optim` function.
`optim.control`	Parameters that controls the `optim` function used for maximum likelihood.

An object of class copreg providing the estimation results. The details of the output components are:

`call`	The matched call.
`coefficients`	The estimated coefficients.
`copula.param`	The estimated copula parameters.
`copula`	The specified copula.
`fitted.values`	The fitted values of the regression.
`residuals`	The residuals from fitted values.
`loglike`	The final estimated maximum log-likelihood value.
`df`	Number of estimated parameters.
`AIC`	AIC values.
`BIC`	BIC values.
`formula`	The formulas used in the regression.
`y`	The input response data.
`n`	The number of observations in the data.
`Model.Matrix`	The used model matrix for each regression formula.

data("simdat.mcgr")
res <- copreg.gamma(f1 = y1 ~ x1+x2,
                    f2 = y2 ~ x1+x2,
                    copula = copula::gumbelCopula(dim=2),
                    data = simdat.mcgr)