It produces diagnostic plots based on (randomised) quantile residuals.
post.check(x, main = "Histogram and Density Estimate of Residuals",
main2 = "Histogram and Density Estimate of Residuals",
xlab = "Quantile Residuals", xlab2 = "Quantile Residuals",
intervals = FALSE, n.sim = 100, prob.lev = 0.05, ...)A fitted copulaReg/copulaSampleSel object.
Title for the plot.
Title for the plot in the second row. This comes into play only when fitting models with two non-binary margins.
Title for the x axis.
Title for the x axis in the second row. As above.
If TRUE then intervals for the qqplots are produced.
Number of replicate datasets used to simulate quantiles of the residual distribution.
Overall probability of the left and right tails of the probabilities' distributions used for interval calculations.
Other graphics parameters to pass on to plotting commands.
It returns the (randomised) quantile residuals for the continuous or discrete margin when fitting a model that involves a binary response.
As above but for first equation (this applies when fitting models with continuous/discrete margins).
As above but for second equation.
If the model fits the response well then the plots should look normally distributed.
When fitting models with discrete and/or continuous margins, four plots will be produced. In this case,
the arguments main2 and xlab2 come into play and allow for different
labelling across the plots.