# copulaSampleSelObject: Fitted copulaSampleSel object In JRM: Joint Regression Modelling

## Description

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

## Value

 `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.

## Author(s)

Maintainer: Giampiero Marra [email protected]

`copulaSampleSel`, `summary.copulaSampleSel`