copulaSampleSelObject: Fitted copulaSampleSel object
In JRM: Joint Regression Modelling

Description Value Author(s) See Also

A fitted semiparametric bivariate object returned by function copulaSampleSel and of class "copulaSampleSel" and "SemiParBIV".

`fit`	List of values and diagnostics extracted from the output of the algorithm.
`gam1`	Univariate fit for equation 1. See the documentation of `mgcv` for full details.
`gam2, gam3, ...`	Univariate fit for equation 2, equation 3, etc.
`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.
`theta`	Estimated dependence parameter linking the two equations.
`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 two equations of the fitted bivariate model (and for the third and fourth equations if present. They are calculated when splines are used.
`bs.mgfit`	List of values and diagnostics extracted from `magic` in `mgcv`.
`conv.sp`	If `TRUE` then the smoothing parameter selection algorithm stopped before reaching the maximum number of iterations allowed.
`wor.c`	Working model quantities.
`eta1, eta2, eta3, ...`	Estimated linear predictors for the two equations (as well as the third and fourth equations if present).
`y1, y2`	Responses of the two equations.
`logLik`	Value of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter estimates.
`respvec`	List containing response vectors.