SemiParBIVProbitObject: Fitted SemiParBIVProbit object
Description
A fitted semiparametric bivariate probit object returned by function SemiParBIVProbit and of class.Value
- fitA list of values and diagnostics extracted from the output of the algorithm. For instance,
fit$argument and fit$S.h return
the estimated parameters and overall penalty matrix scaled by its smoothing parameters, for the bivariate probit model. See the
documentation of trust for diagnostics. - gam1A univariate GAM object for equation 1. See the documentation of
mgcv for full details. - gam2A univariate GAM object for equation 2.
- gam2.1A univariate GAM object for equation 2, estimated using a Heckman-type sample selection correction procedure (
selection=TRUE). - p.weightsPrior weights used during model fitting.
- spEstimated smoothing parameters of the smooth components for the fitted bivariate probit model.
- iter.spNumber of iterations performed for the smoothing parameter estimation step.
- iter.ifNumber of iterations performed in the initial step of the Fisher scoring algorithm.
- rhoEstimated correlation coefficient between the two equations.
- nSample size.
- n.selNumber of selected observations in the sample selection case.
- X1It returns the design matrix associated with the first linear predictor.
- X2It returns the design matrix associated with the second linear predictor.
- X1.d2Number of columns of the design matrix for equation 1. This is used for internal calculations.
- X2.d2Number of columns of the design matrix for equation 2.
- l.sp1Number of smooth components in equation 1.
- l.sp2Number of smooth components in equation 2.
- HePenalized hessian.
- HeShUnpenalized hessian.
- VbInverse of the penalized hessian. This corresponds to the Bayesian variance-covariance matrix used for `confidence' interval calculations.
- FThis is given by
Vb*HeSh. - t.edfTotal degrees of freedom of the estimated bivariate probit model. It is calculated as
sum(diag(F)). - bs.mgfitA list of values and diagnostics extracted from
magic. - conv.spIf
TRUE then the smoothing parameter selection algorithm converged. - wor.cIt contains the working model quantities given by the square root of
the weight matrix times the pseudo-data vector and design matrix,
rW.Z and rW.X. - p11,p10,p01,p00Distribution function of a bivariate normal with zero means, unit variances and correlation
rho evaluated
at (y1=1,y2=1), (y1=1,y2=0), (y1=0,y2=1) and (y1=0,y2=0). - eta1,eta2The estimated linear predictors for the two equations.
- datIt returns the full design matrix associated with the observed binary variables and two linear predictors.
- selThis is used for internal calculations.
- massesProbabilities associated with the
K biavariate mass points. - KNumber of bivariate mass points.
- iter.npRENumber of iterations performed for a starting value procedure when using nonparametric random effect.
- iter.npREspNumber of cumulative extra-iterations performed in the
smoothing parameter estimation loop when using nonparametric random effects.
- npREThis is used for internal calculations.
- logLIt returns the value of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter
estimates.
- eb.u1,eb.u2Estimated nonparametric random effects for each identifier.
- Eb.u1,Eb.u2Estimated nonparametric random effects for each observation.
- idIt represents the individual identifier.
- uidfIt returns the number of observations within each group.
- T.svThis is used for internal calculations.