A fitted semiparametric bivariate object returned by function copulaSampleSel and of class "copulaSampleSel" and "SemiParBIV".
List of values and diagnostics extracted from the output of the algorithm.
Univariate fit for equation 1. See the documentation of mgcv for full details.
Univariate fit for equation 2, equation 3, etc.
The coefficients of the fitted model.
Prior weights used during model fitting.
Estimated smoothing parameters of the smooth components.
Number of iterations performed for the smoothing parameter estimation step.
Number of iterations performed in the initial step of the algorithm.
Number of iterations performed within the smoothing parameter estimation step.
Estimated dependence parameter linking the two equations.
Sample size.
Design matrices associated with the linear predictors.
Number of columns of X1, X2, X3, etc.
Number of smooth components in the equations.
Penalized -hessian/Fisher. This is the same as HeSh for unpenalized models.
Unpenalized -hessian/Fisher.
Inverse of He. This corresponds to the Bayesian variance-covariance matrix
used for confidence/credible interval calculations.
This is obtained multiplying Vb by HeSh.
Total degrees of freedom of the estimated bivariate model. It is calculated as sum(diag(F)).
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.
List of values and diagnostics extracted from magic in mgcv.
If TRUE then the smoothing parameter selection algorithm stopped before reaching the maximum number of iterations allowed.
Working model quantities.
Estimated linear predictors for the two equations (as well as the third and fourth equations if present).
Responses of the two equations.
Value of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter estimates.
List containing response vectors.