SemiParBIVProbit (version 3.3)

SemiParBIVProbitObject: Fitted SemiParBIVProbit object

Description

A fitted semiparametric bivariate probit object returned by function SemiParBIVProbit and of class "SemiParBIVProbit".

Value

• fit
  List 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.
• gam1
  Univariate fit for equation 1. See the documentation of mgcv for full details.
• gam2
  Univariate fit for equation 2.
• gam2.1
  Univariate fit for equation 2, estimated using an adaptation of the Heckman sample selection correction procedure when Method = "BSS".
• gam3
  Univariate fit for equation 3. This is available when the association parameter is modelled as functions of covariates.
• coefficients
  The 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 association coefficient.
• 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.
• rho
  Estimated correlation coefficient between the two equations, when a bivariate normal model is employed.
• theta
  Estimated copula parameter linking the two equations.
• n
  Sample size.
• n.sel
  Number of selected observations in the sample selection model case.
• X1, X2
  Design matrices associated with the first and second linear predictors.
• X1.d2, X2.d2
  Number of columns of X1 and X2.
• l.sp1, l.sp2
  Number of smooth components in equations 1 and 2.
• 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 given by Vb*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 equation as well if the dependence parameter is modelled as function of covariates). 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.
• p11, p10, p01, p00
  Model 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).
• p0
  Distribution function of a standardised univariate normal evaluated at y_1 = 0. This is only provided when Method = "BSS".
• eta1, eta2, eta3
  Estimated linear predictors for the two equations (as well as the third equation if present).
• y1, y2
  Responses of the two equations.
• xi1, xi2
  Estimated shape parameters of the link functions of the two equations if an asymmetric link approach is used.
• logLik
  Value of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter estimates.
• good
  Indicator variable indicating the observations actually used in model fitting.
• respvec
  List containing response vectors.
• X2s
  Full design matrix of outcome equation in sample selection case.
• OR, GM
  Odds ratio and Gamma measure. See summary.SemiParBIVProbit for details.

See Also

SemiParBIVProbit, plot.SemiParBIVProbit, summary.SemiParBIVProbit, predict.SemiParBIVProbit