summary.gjrm: gjrm summary
In KironmoyDas/KD-STAT0035-GMupdate: Generalised Joint Regression Modelling

Description Usage Arguments Details Value Author(s)

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

## 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"), ...)

`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.

print.summary.gjrm prints model term summaries.

`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 θ.
`CIsig1,CIsig2,CInu1,CInu2`	Intervals for distribution specific parameters