KD-STAT0035-GMupdate: Generalised Joint Regression Modelling

Description Usage Arguments Details Value Author(s) See Also

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, ...)

`x`	A fitted `gjrm` object.
`main`	Title for the plot.
`main2`	Title for the plot in the second row. This comes into play only when fitting models with two non-binary margins.
`xlab`	Title for the x axis.
`xlab2`	Title for the x axis in the second row. As above.
`intervals`	If `TRUE` then intervals for the qqplots are produced.
`n.sim`	Number of replicate datasets used to simulate quantiles of the residual distribution.
`prob.lev`	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.

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.

`qr`	It returns the (randomised) quantile residuals for the continuous or discrete margin when fitting a model that involves a binary response.
`qr1`	As above but for first equation (this applies when fitting models with continuous/discrete margins).
`qr2`	As above but for second equation.