SemiParSampleSelObject: Fitted SemiParSampleSel object
Description
A fitted semiparametric sample selection object returned by function SemiParSampleSel and of class "SemiParSampleSel".Value
- fitList of values and diagnostics extracted from the output of the algorithm. For instance,
fit$gradient and fit$S.h return
the gradient vector and overall penalty matrix scaled by its smoothing parameters, for the bivariate model. See the
documentation of trust for details on the diagnostics provided. - gam1Univariate fit for selection equation. See the documentation of
mgcv for full details. - gam2Univariate fit for outcome equation.
- gam2.1Univariate fit for equation 2, estimated using an adaptation of the Heckman sample selection correction procedure.
- coefficientsThe coefficients of the fitted semiparametric sample selection model.
- weightsPrior weights used during model fitting.
- spEstimated smoothing parameters of the smooth components for the fitted sample selection model.
- 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 inside smoothing parameter estimation step.
- start.vStarting values for all model parameters of the semiparametric sample selection algorithm. These are obtained using the
Heckman sample selection
correction approach when starting values are not provided.
- phiEstimated dispersion for the response of the outcome equation. In the normal bivariate case, this corresponds to the variance.
- sigmaEstimated standard deviation for the response of the outcome equation in the case of normal marginal distribution of the outcome.
- shapeEstimated shape parameter for the response of the outcome equation in the case of gamma marginal distribution of the outcome.
- nuEstimated shape parameter for the response of the outcome equation in the case of a discrete distribution.
- thetaEstimated coefficient linking the two equations. In the normal bivariate case, this corresponds to the correlation coefficient.
- nSample size.
- n.selNumber of selected observations.
- X1Design matrix associated with the first linear predictor.
- X2Design 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. - BivDType of bivariate copula distribution employed.
- marginsMargins used in the bivariate copula specification.
- t.edfTotal degrees of freedom of the estimated sample selection model. It is calculated as
sum(diag(F)). - bs.mgfitA list of values and diagnostics extracted from
magic in mgcv. - conv.spIf
TRUE then the smoothing parameter selection algorithm converged. - wor.cWorking model quantities given by the square root of
the weight matrix times the pseudo-data vector and design matrix,
rW.Z and rW.X. - eta1,eta2Estimated linear predictors for the two equations.
- y1Binary outcome of the selection equation.
- y2Dependent variable of the outcome equation.
- logLikValue of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter
estimates.
- fpIf
TRUE, then a fully parametric model was fitted. - pPen1, pPen2List specifying any penalties to be applied to the parametric model
terms of the model equations.
- X2sFull design matrix of outcome equation.