A fitted gamlss object returned by function gamlss and of class "gamlss" and "SemiParBIVProbit".
List of values and diagnostics extracted from the output of the algorithm.
Univariate starting values' fits.
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.
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 model's equations.
Working model quantities.
Estimated linear predictors.
Response.
Value of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter estimates.
gamlss, plot.SemiParBIVProbit, summary.gamlss, predict.SemiParBIVProbit