gamlssObject: Fitted gamlssObject object
In SemiParBIVProbit: Semiparametric Copula Regression Models

A fitted gamlss object returned by function gamlss and of class "gamlss" and "SemiParBIVProbit".

`fit`	List of values and diagnostics extracted from the output of the algorithm.
`gam1, gam2, gam3`	Univariate starting values' fits.
`coefficients`	The coefficients of the fitted model.
`weights`	Prior weights used during model fitting.
`sp`	Estimated smoothing parameters of the smooth components.
`iter.sp`	Number of iterations performed for the smoothing parameter estimation step.
`iter.if`	Number of iterations performed in the initial step of the algorithm.
`iter.inner`	Number of iterations performed within the smoothing parameter estimation step.
`n`	Sample size.
`X1, X2, X3, ...`	Design matrices associated with the linear predictors.
`X1.d2, X2.d2, X3.d2, ...`	Number of columns of `X1`, `X2`, `X3`, etc.
`l.sp1, l.sp2, l.sp3, ...`	Number of smooth components in the equations.
`He`	Penalized -hessian/Fisher. This is the same as `HeSh` for unpenalized models.
`HeSh`	Unpenalized -hessian/Fisher.
`Vb`	Inverse of `He`. This corresponds to the Bayesian variance-covariance matrix used for confidence/credible interval calculations.
`F`	This is obtained multiplying Vb by HeSh.
`t.edf`	Total degrees of freedom of the estimated bivariate model. It is calculated as `sum(diag(F))`.
`edf1, edf2, edf3, ...`	Degrees of freedom for the model's equations.
`wor.c`	Working model quantities.
`eta1, eta2, eta3, ...`	Estimated linear predictors.
`y1`	Response.
`logLik`	Value of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter estimates.