summary.gjrm: gjrm summary

In GJRM: Generalised Joint Regression Modelling

gjrm summary

Description

It takes a fitted gjrm object and produces some summaries from it.

Usage

## S3 method for class 'gjrm'
summary(object, n.sim = 100, prob.lev = 0.05, ...)
   


## S3 method for class 'summary.gjrm'
print(x, digits = max(3, getOption("digits") - 3), 
           signif.stars = getOption("show.signif.stars"), ...)
 

Arguments

object	A fitted gjrm object.
x	summary.gjrm object produced by summary.gjrm().
n.sim	The number of simulated coefficient vectors from the posterior distribution of the estimated model parameters. This is used to calculate intervals for the association parameter, dispersion coefficient etc. It may be increased if more precision is required.
prob.lev	Probability of the left and right tails of the posterior distribution used for interval calculations.
digits	Number of digits printed in output.
signif.stars	By default significance stars are printed alongside output.
...	Other arguments.

Details

print.summary.gjrm prints model term summaries.

Value

tableP1	Table containing parametric estimates, their standard errors, z-values and p-values for equation 1.
tableP2, tableP3, ...	As above but for equation 2 and equations 3 and 4 if present.
tableNP1	Table of nonparametric summaries for each smooth component including effective degrees of freedom, estimated rank, approximate Wald statistic for testing the null hypothesis that the smooth term is zero and corresponding p-value, for equation 1.
tableNP2, tableNP3, ...	As above but for equation 2 and equations 3 and 4 if present.
n	Sample size.
theta	Estimated dependence parameter linking the two equations.
sigma1, sigma2	Estimated distribution specific parameters for equations 1 and 2.
nu1, nu2	Estimated distribution specific parameters for equations 1 and 2.
formula1, formula2, formula3, ...	Formulas used for the model equations.
l.sp1, l.sp2, l.sp3, ...	Number of smooth components in model equations.
t.edf	Total degrees of freedom of the estimated bivariate model.
CItheta	Interval(s) for \theta.
CIsig1, CIsig2, CInu1, CInu2	Intervals for distribution specific parameters

WARNINGS

Note that the Kendall's tau (and related interval), as implemented here, is a valid measure of dependence for continuous margins and it will only provide a crude indication of dependence in other cases.

Author(s)

Maintainer: Giampiero Marra [e-mail]

GJRM documentation built on Oct. 25, 2024, 5:07 p.m.