plot.glmmNPML: Plot Diagnostics for objects of class glmmNPML or glmmGQ
In npmlreg: Nonparametric Maximum Likelihood Estimation for Random Effect Models

Description Usage Arguments Details Value Author(s) References See Also Examples

The functions alldist and allvc produce objects of type glmmGQ, if Gaussian quadrature (Hinde, 1982, random.distribution="gq") was applied for computation, and objects of class glmmNPML, if parameter estimation was carried out by nonparametric maximum likelihood (Aitkin, 1996a, random.distribution="np"). The functions presented here give some useful diagnostic plotting functionalities to analyze these objects.

## S3 method for class 'glmmNPML'
plot(x, plot.opt = 15, noformat=FALSE, ...)
## S3 method for class 'glmmGQ'
plot(x, plot.opt = 3, noformat=FALSE, ...)

`x`	a fitted object of class `glmmNPML` or `glmmGQ`.
`plot.opt`	an integer with values 0 <= `plot.opt` <=15.
`noformat`	if `TRUE`, then any formatting of the plots is omitted (useful if the user wants to include the plots into a panel of several other plots, possibly generated by other functions).
`...`	further arguments which will mostly not have any effect (and are included only to ensure compatibility with the generic `plot()`- function.)

See the help pages to alldist and the vignette (Einbeck & Hinde, 2007). It is sufficient to write plot instead of plot.glmmNPML or plot.glmmGQ, since the generic plot function provided in R automatically selects the right model class.

For class glmmNPML: Depending on the choice of plot.opt, a subset of the following four plots:

`1`	Disparity trend.
`2`	EM Trajectories.
`3`	Empirical Bayes Predictions against observed response.
`4`	Individual posterior probabilities.

The number given in plot.opt is transformed into a binary number indicating which plots are to be selected. The first digit (from the right!) refers to plot 1, the second one to plot 2, and so on. For example, plot.opt=4 gives the binary number 0100 and hence selects just plot 3.

For class glmmGQ: Depending on the choice of plot.opt, a subset of plots 1 and 3. Again, the number is transformed into binary coding, yielding only the disparity trend for plot.opt=1, only the EBP's for plot.opt=2, and both plots for plot.opt=3.

Jochen Einbeck and John Hinde (2007)

Aitkin, M. (1996a). A general maximum likelihood analysis of overdispersion in generalized linear models. Statistics and Computing 6, 251-262.

Einbeck, J., and Hinde, J.: Nonparametric maximum likelihood estimation for random effect models in R. Vignette to R package npmlreg. Type vignette("npmlreg-v") to open it.

Hinde, J. (1982). Compound Poisson regression models. Lecture Notes in Statistics 14, 109-121.

alldist, allvc

data(galaxies, package="MASS")
gal<-as.data.frame(galaxies)
galaxy.np4u <- alldist(galaxies/1000~1,random=~1,k=4,tol=0.5,data=gal,lambda=1)
predict(galaxy.np4u, type="response") # EBP on scale of responses

plot(galaxy.np4u,  plot.opt=4) # plots only EBP vs.  response
plot(galaxy.np4u,  plot.opt=3) # gives same output as given by default when executing alldist
plot(galaxy.np4u)              # gives all four plots.