bgevaObject: Fitted bgeva object
Description
A fitted Binary Generalized Extreme Value Additive object returned by function bgeva 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 model. See the
documentation of trust for diagnostics. - coefficientsThe coefficients of the fitted model provided as follows. Parametric and regression spline
coefficients.
- gam.fitA univariate logistic additive model object. See the documentation of
mgcv for full details. - spEstimated smoothing parameters of the smooth components for the fitted model.
- fpIf
TRUE, then a fully parametric model was fitted. - 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.
- tauThe tail parameter of the link function.
- nSample size.
- XIt returns the design matrix associated with the linear predictor.
- XrIt returns the design matrix actually used in model fitting.
- goodIt returns a vector indicating which observations have been discarded in the final iteration.
- X.d2Number of columns of the design matrix. This is used for internal calculations.
- l.spNumber of smooth components.
- 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 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. - etaThe estimated linear predictor.
- logLIt returns the value of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter
estimates.