summary.glmmNPML: Summarizing finite mixture regression fits
In npmlreg: Nonparametric Maximum Likelihood Estimation for Random Effect Models

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

These functions are the summary and print methods for objects of type glmmNPML and glmmGQ.

## S3 method for class 'glmmNPML'
summary(object, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'glmmGQ'
summary(object, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'glmmNPML'
print(x, digits=max(3,getOption('digits')-3), ...)
## S3 method for class 'glmmGQ'
print(x, digits=max(3,getOption('digits')-3),  ...)

`object`	a fitted object of class `glmmNPML` or `glmmGQ`.
`x`	a fitted object of class `glmmNPML` or `glmmGQ`.
`digits`	number of digits; applied on various displayed quantities.
`...`	further arguments, which will mostly be ignored.

The summary...- and print... -functions invoke the generic UseMethod(...) function and detect the right model class automatically. In other words, it is enough to write summary(...) or print(...).

Prints regression output or summary on screen.

Objects returned by summary.glmmNPML or summary.glmmGQ are essentially identical to objects of class glmmNPML or glmmGQ. However, their $coef component contains the parameter standard errors and t values (taken from the GLM fitted in the last EM iteration), and they have two additional components $dispersion and $lastglmsumm providing the estimated dispersion parameter and a summary of the glm fitted in the last EM iteration.

Please note that the provided parameter standard errors tend to be underestimated as the uncertainty due to the EM algorithm is not incorporated into them. According to Aitkin et al (2009), Section 7.5, page 440, more accurate standard errors can be obtained by dividing the (absolute value of the) parameter estimate through the square root of the change in disparity when omitting/not omitting the variable from the model.

originally from Ross Darnell (2002), modified and prepared for publication by Jochen Einbeck and John Hinde (2007).

Aitkin, M., Francis, B. and Hinde, J. (2009). Statistical Modelling in R. Oxford Statistical Science Series, Oxford, UK.

alldist, allvc, summary, print, family.glmmNPML