Dear Bart:
Testing the improvement of fit between two nested models is
equivalent to testing one or more constraints. Say I have the following nested models:
Model 1: y = a0 + a1*x1
Model 2: y = b0 + b1*x1 + b2*x2 + b3*x3
Model 2 is exactly the same as model 1 if b2 and b3 are zero. In which case both x2 and x3 added
nothing to the model and could have been left out. In other words model 2 is not an improvement
over model 1 if we cannot reject the constraint that b2 and b3 are zero.
Sometimes the constraints are more complicated, or involve the shape parameter (e.g. some -streg-
models are nested that way) but the principle remains the same.
So the advise given to you by the Stata FAQ also applies to testing improvements of fit.
HTH,
Maarten
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting adress:
Buitenveldertselaan 3 (Metropolitan), room Z214
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
Bart Vanneste wrote:
> How can I test the significance of model improvement if only
> the log pseudolikelihood is given?
>
> Can I still use LRtest?
<snip>
> On the STATA website, it is argued that lrtest is not valid
> when using pseudolikelihoods. The argument seems to focus on
> testing of a group of coefficients, and it's not clear whether
> the lrtest is also invalid when assessing (increases in) model
> fit. See:
> http://www.stata.com/support/faqs/stat/lrtest.html
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