SemiParSampleSel (version 1.5)

SemiParSampleSelObject: Fitted SemiParSampleSel object

Description

A fitted semiparametric sample selection object returned by function SemiParSampleSel and of class "SemiParSampleSel".

Value

fit

List 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.

gam1

Univariate fit for selection equation. See the documentation of mgcv for full details.

gam2,gam3,gam4,gam5

Univariate fit for the outcome equation and equations 3, 4 and 5 when present.

gam2.1

Univariate fit for equation 2, estimated using an adaptation of the Heckman sample selection correction procedure.

coefficients

The coefficients of the fitted semiparametric sample selection model.

weights

Prior weights used during model fitting.

sp

Estimated smoothing parameters of the smooth components for the fitted sample selection model.

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 inside smoothing parameter estimation step.

start.v

Starting 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 and the dependence parameter is not specified as a function of a linear predictor.

phi

Estimated dispersion for the response of the outcome equation. In the normal bivariate case, this corresponds to the variance.

sigma

Estimated standard deviation for the response of the outcome equation in the case of normal marginal distribution of the outcome.

shape

Estimated shape parameter for the response of the outcome equation in the case of gamma marginal distribution of the outcome.

nu

Estimated shape parameter for the response of the outcome equation in the case of a discrete distribution.

theta

Estimated coefficient linking the two equations. In the normal bivariate case, this corresponds to the correlation coefficient.

n

Sample size.

n.sel

Number of selected observations.

X1,X2,X3,X4,X5

Design matrices associated with the linear predictors.

X1.d2,X2.d2,X3.d2,X4.d2,X5.d2

Number of columns of the design matrices.

l.sp1,l.sp2,l.sp3,l.sp4,l.sp5

Number of smooth components in the equations.

He

Penalized hessian.

HeSh

Unpenalized hessian.

Vb

Inverse of the penalized hessian. This corresponds to the Bayesian variance-covariance matrix used for `confidence' interval calculations.

F

This is given by Vb*HeSh.

BivD

Type of bivariate copula distribution employed.

margins

Margins used in the bivariate copula specification.

t.edf

Total degrees of freedom of the estimated sample selection model. It is calculated as sum(diag(F)).

bs.mgfit

A list of values and diagnostics extracted from magic in mgcv.

conv.sp

If TRUE then the smoothing parameter selection algorithm converged.

wor.c

Working model quantities.

eta1,eta2

Estimated linear predictors for the two equations.

y1

Binary outcome of the selection equation.

y2

Dependent variable of the outcome equation.

logLik

Value of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter estimates.

fp

If TRUE, then a fully parametric model was fitted.

X2s

Full design matrix of outcome equation.

See Also

aver, SemiParSampleSel, plot.SemiParSampleSel, predict.SemiParSampleSel, summary.SemiParSampleSel