Message-ID: <alpine.OSX.2.00.1101071419430.27483@qatna>
Date: 2011-01-07T13:26:08Z
From: sovo0815 at gmail.com
Subject: Different LLRs on multinomial logit models in R and SPSS
In-Reply-To: <8D4AC2A1-ABCB-4B51-B057-05A98745C28E@comcast.net>
On Thu, 6 Jan 2011, David Winsemius wrote:
> On Jan 6, 2011, at 11:23 AM, S?ren 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.
S?ren