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
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