lmm WITHOUT random factor (lme4)
On Fri, 18 Mar 2011, ONKELINX, Thierry wrote:
What worries me is that the loglikelihood of a lm() model and the equivalent gls() model is different. Although both models should be mathematically identical. Assuming that the loglikelihood is calculated on the same way within a package, I therefore have more confidence in comparing two models from the same package, thus gls() versus lme(). Furthermore, I get an error when doing an anova between a lm() and a lme() model.
logLik(fm)
'log Lik.' -950.1465 (df=3)
logLik(fm, REML=T)
'log Lik.' -946.8318 (df=3)
anova(fm1, fm0, fm)
Model df AIC BIC logLik Test L.Ratio p-value fm1 1 4 1794.465 1807.192 -893.2325 fm0 2 3 1899.664 1909.209 -946.8318 1 vs 2 107.1986 <.0001 fm 3 3 1899.664 1909.209 -946.8318
| David Duffy (MBBS PhD) ,-_|\ | email: davidD at qimr.edu.au ph: INT+61+7+3362-0217 fax: -0101 / * | Epidemiology Unit, Queensland Institute of Medical Research \_,-._/ | 300 Herston Rd, Brisbane, Queensland 4029, Australia GPG 4D0B994A v