bgevaObject: Fitted bgeva object
Description
A fitted Binary Generalized Extreme Value Additive object returned by function bgeva and of class.Value
- fit
- A 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. - coefficients
- The coefficients of the fitted model provided as follows. Parametric and regression spline
coefficients.
- gam.fit
- A univariate logistic additive model object. See the documentation of
mgcv for full details. - sp
- Estimated smoothing parameters of the smooth components for the fitted model.
- fp
- If
TRUE, then a fully parametric model was fitted. - 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.
- tau
- The tail parameter of the link function.
- n
- Sample size.
- X
- It returns the design matrix associated with the linear predictor.
- Xr
- It returns the design matrix actually used in model fitting.
- good
- It returns a vector indicating which observations have been discarded in the final iteration.
- X.d2
- Number of columns of the design matrix. This is used for internal calculations.
- l.sp
- Number of smooth components.
- 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. - t.edf
- Total degrees of freedom of the estimated model. It is calculated as
sum(diag(F)). - bs.mgfit
- A list of values and diagnostics extracted from
magic. - conv.sp
- If
TRUE then the smoothing parameter selection algorithm converged. - wor.c
- It 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. - eta
- The estimated linear predictor.
- logL
- It returns the value of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter
estimates.