Fitted gamlssObject object
A fitted gamlss object returned by function gamlss and of class "gamlss" and "SemiParBIV".
fit: List of values and diagnostics extracted from the output of the algorithm.
gam1, gam2, gam3: Univariate starting values' fits.
coefficients: The coefficients of the fitted model.
weights: Prior weights used during model fitting.
sp: Estimated smoothing parameters of the smooth components.
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 within the smoothing parameter estimation step.
n: Sample size.
X1, X2, X3, ...: Design matrices associated with the linear predictors.
X1.d2, X2.d2, X3.d2, ...: Number of columns of X1, X2, X3, etc.
l.sp1, l.sp2, l.sp3, ...: Number of smooth components in the equations.
He: Penalized -hessian/Fisher. This is the same as HeSh for unpenalized models.
HeSh: Unpenalized -hessian/Fisher.
Vb: Inverse of He. This corresponds to the Bayesian variance-covariance matrix used for confidence/credible interval calculations.
F: This is obtained multiplying Vb by HeSh.
t.edf: Total degrees of freedom of the estimated bivariate model. It is calculated as sum(diag(F)).
edf1, edf2, edf3, ...: Degrees of freedom for the model's equations.
wor.c: Working model quantities.
eta1, eta2, eta3, ...: Estimated linear predictors.
y1: Response.
logLik: Value of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter estimates.
Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk
gamlss, summary.gamlss