[R] Different LLRs on multinomial logit models in R and SPSS

[sovo0815 at gmail.com]

On Thu, 6 Jan 2011, David Winsemius wrote:

> On Jan 6, 2011, at 11:23 AM, Sören Vogel wrote:
>
>> Thanks for your replies. I am no mathematician or statistician by far,
>> however, it appears to me that the actual value of any of the two LLs
>> is indeed important when it comes to calculation of
>> Pseudo-R-Squared-s. If Rnagel devides by (some transformation of) the
>> actiual value of llnull then any calculation of Rnagel should differ.
>> How come? Or is my function wrong? And if my function is right, how
>> can I calculate a R-Squared independent from the software used?
>
> You have two models in that function, the null one with ".~ 1" and the 
> origianl one and you are getting a ratio on the likelihood scale (which is a 
> difference on the log-likelihood or deviance scale).

If this is the case, calculating 'fit' indices for those models 
must end up in different fit indices depending on software:

n <- 143
ll1 <- 135.02
ll2 <- 129.8
# Rcs
(Rcs <- 1 - exp( (ll2 - ll1) / n ))
# Rnagel
Rcs / (1 - exp(-ll1/n))
ll3 <- 204.2904
ll4 <- 199.0659
# Rcs
(Rcs <- 1 - exp( (ll4 - ll3) / n ))
# Rnagel
Rcs / (1 - exp(-ll3/n))

The Rcs' are equal, however, the Rnagel's are not. Of course, this 
is no question, but I am rather confused. When publishing results 
I am required to use fit indices and editors would complain that 
they differ.

Sören