gamlssObject: Fitted gamlssObject object

Description Value Author(s) See Also

Description

A fitted gamlss object returned by function gamlss and of class "gamlss" and "SemiParBIV".

Value

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.

Author(s)

Maintainer: Giampiero Marra [email protected]

See Also

gamlss, summary.gamlss


JRM documentation built on July 13, 2017, 5:03 p.m.