summary.glmmNPML {npmlreg}

Summarizing finite mixture regression fits

Description

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

Usage

## 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),  ...)

Arguments

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.

Details

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(...).

Value

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.

Note

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.

Author(s)

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

References

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

See Also

alldist, allvc, summary, print, family.glmmNPML