summary.gjrm {GJRM} | R Documentation |
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 |
x |
|
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. |
tau |
Estimated Kendall's tau dependence measure between 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 , CItau |
Interval(s) for |
CIsig1 , CIsig2 , CInu1 , CInu2 |
Intervals for distribution specific parameters |
WARNINGS
Note that the summary output will also indeed provide the Kendall's tau and related interval. This is a valid measure of dependence for continuous margins but it may not for discrete margins, for instance. However, it is still displayed for the sake of keeping the printed output consistent with that of other models in the package. Also, it still provides an approximate measure of dependence under certan scenarios.
Author(s)
Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk