SemiParBIVProbitObject: Fitted SemiParBIVProbit object
Description
A fitted semiparametric bivariate probit object returned by function SemiParBIVProbit and of class "SemiParBIVProbit".Value
- fitList of values and diagnostics extracted from the output of the algorithm. For instance,
fit$gradient, fit$Fisher and fit$S.h return
the gradient vector, Fisher information (when used) and overall penalty matrix scaled
by its smoothing parameters, for the fitted bivariate probit model. See the
documentation of trust for details on the diagnostics provided. - gam1Univariate fit for equation 1. See the documentation of
mgcv for full details. - gam2, gam3, ...Univariate fit for equation 2 and equations 3 and 4 (these are available when the dispersion and association parameters
are modelled
as functions of covariates).
- coefficientsThe coefficients of the fitted model. They are given in the following
order: parametric and regression spline (if present)
coefficients for the first equation, parametric and regression spline
coefficients for the second equation, and dispersion parameter (or coefficients for the third equation) and
association coefficient (or coefficients for the fourth equation).
- weightsPrior weights used during model fitting.
- spEstimated smoothing parameters of the smooth components.
- iter.spNumber of iterations performed for the smoothing parameter estimation step.
- iter.ifNumber of iterations performed in the initial step of the algorithm.
- iter.innerNumber of iterations performed within the smoothing parameter estimation step.
- thetaEstimated dependence parameter linking the two equations.
- nSample size.
- n.selNumber of selected observations in the sample selection model case.
- 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.
- HePenalized -hessian/Fisher. This is the same as
HeSh for unpenalized models. - HeShUnpenalized -hessian/Fisher.
- VbInverse of
He. This corresponds to the Bayesian variance-covariance matrix
used for confidence/credible interval calculations. - t.edfTotal 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.mgfitList of values and diagnostics extracted from
magic in mgcv. - conv.spIf
TRUE then the smoothing parameter selection algorithm stopped before reaching the maximum number of iterations allowed. - wor.cWorking model quantities.
- p11, p10, p01, p00Model probabilities evaluated at (y_1 = 1, y_2 = 1), (y_1 = 1, y_2 = 0), (y_1 = 0, y_2 = 1) and (y_1 = 0, y_2 = 0).
- p1, p2Marginal probabilities.
- p1n, p2nNaive marginal probabilities. These are only provided when
Method = "BSS" and are built using two separate fits. - eta1, eta2, eta3, ...Estimated linear predictors for the two equations (as well as the third and fourth equations if present).
- y1, y2Responses of the two equations.
- logLikValue of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter
estimates.
- respvecList containing response vectors.
- X2sFull design matrix of outcome equation for sample selection case.
- OR, GMOdds ratio and Gamma measure. See
summary.SemiParBIVProbit for details.