Chris/Ben The lack of effect of the REML parameter is simply explained by the fact you are fitting a binomial model. This causes the lmer call to default to a glmer call in which the REML parameter is ignored. I also note that you are specifying order/family in the random term, which I assume are the taxanomic definitions of family and order. As family is completey nested in order so that order:family is as unique as family, no additional variance is explained by order over family so I believe that you should just be able to specify (1|family) for your random intercept. Regards Andrew Dr Andrew Crowe Lancaster Environment Centre Lancaster University Lancaster LA1 4YQ UK
________________________________ From: r-sig-ecology-boun...@r-project.org on behalf of Chris Mcowen Sent: Thu 05/08/2010 2:04 PM To: Ben Bolker Cc: r-sig-ecology@r-project.org Subject: Re: [R-sig-eco] AIC / BIC vs P-Values in lmer I have just tried it with REML=FALSE and once again there is no difference in the AIC/BIC values between the two models? I have given two examples this time but have tried it with 10 models with no difference. Thanks, Chris 1 MODEL WITH REML=FALSE > model01 <- lmer(threatornot~1+(1|order/family) + seasonality + > pollendispersal + breedingsystem*fruit + habit + lifeform + woodyness, > family=binomial,REML=FALSE ) Generalized linear mixed model fit by the Laplace approximation Formula: threatornot ~ 1 + (1 | order/family) + seasonality + pollendispersal + breedingsystem * fruit + habit + lifeform + woodyness AIC BIC logLik deviance 1399 1479 -683.6 1367 Random effects: Groups Name Variance Std.Dev. family:order (Intercept) 0.27526 0.52466 order (Intercept) 0.00000 0.00000 Number of obs: 1116, groups: family:order, 43; order, 9 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.384574 0.734960 0.523 0.60079 seasonality2 -1.127996 0.353013 -3.195 0.00140 ** pollendispersal2 0.693255 0.314600 2.204 0.02755 * breedingsystem2 0.761067 0.493404 1.542 0.12296 breedingsystem3 1.226269 0.557236 2.201 0.02776 * fruit2 1.047648 0.616723 1.699 0.08937 . habit2 -1.146334 0.551682 -2.078 0.03772 * habit3 -0.731207 0.872805 -0.838 0.40216 habit4 -0.190900 0.551427 -0.346 0.72920 lifeform2 -0.295342 0.182667 -1.617 0.10592 lifeform3 -0.376204 0.501825 -0.750 0.45345 woodyness2 0.006274 0.390241 0.016 0.98717 breedingsystem2:fruit2 -1.273811 0.651011 -1.957 0.05039 . breedingsystem3:fruit2 -1.633424 0.744563 -2.194 0.02825 * MODEL WITHOUT REML=FALSE model126 <- lmer(threatornot~1+(1|order/family) + seasonality + pollendispersal + breedingsystem*fruit + habit + lifeform + woodyness, family=binomial) Generalized linear mixed model fit by the Laplace approximation Formula: threatornot ~ 1 + (1 | order/family) + seasonality + pollendispersal + breedingsystem * fruit + habit + lifeform + woodyness AIC BIC logLik deviance 1399 1479 -683.6 1367 Random effects: Groups Name Variance Std.Dev. family:order (Intercept) 0.27526 0.52466 order (Intercept) 0.00000 0.00000 Number of obs: 1116, groups: family:order, 43; order, 9 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.384574 0.734960 0.523 0.60079 seasonality2 -1.127996 0.353013 -3.195 0.00140 ** pollendispersal2 0.693255 0.314600 2.204 0.02755 * breedingsystem2 0.761067 0.493404 1.542 0.12296 breedingsystem3 1.226269 0.557236 2.201 0.02776 * fruit2 1.047648 0.616723 1.699 0.08937 . habit2 -1.146334 0.551682 -2.078 0.03772 * habit3 -0.731207 0.872805 -0.838 0.40216 habit4 -0.190900 0.551427 -0.346 0.72920 lifeform2 -0.295342 0.182667 -1.617 0.10592 lifeform3 -0.376204 0.501825 -0.750 0.45345 woodyness2 0.006274 0.390241 0.016 0.98717 breedingsystem2:fruit2 -1.273811 0.651011 -1.957 0.05039 . breedingsystem3:fruit2 -1.633424 0.744563 -2.194 0.02825 * 2 MODEL WITH REML=FALSE > model02 <- lmer(threatornot~1+(1|order/family) + seasonality + woodyness, > family=binomial,REML=FALSE ) Generalized linear mixed model fit by the Laplace approximation Formula: threatornot ~ 1 + (1 | order/family) + seasonality + woodyness AIC BIC logLik deviance 1395 1420 -692.6 1385 Random effects: Groups Name Variance Std.Dev. family:order (Intercept) 0.49348 0.70248 order (Intercept) 0.00000 0.00000 Number of obs: 1116, groups: family:order, 43; order, 9 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.6034 0.4227 1.427 0.15346 seasonality2 -1.1421 0.3453 -3.308 0.00094 *** woodyness2 0.5113 0.2559 1.998 0.04572 * MODEL WITHOUT REML=FALSE model03 <- lmer(threatornot~1+(1|order/family) + seasonality + woodyness, family=binomial) Generalized linear mixed model fit by the Laplace approximation Formula: threatornot ~ 1 + (1 | order/family) + seasonality + woodyness AIC BIC logLik deviance 1395 1420 -692.6 1385 Random effects: Groups Name Variance Std.Dev. family:order (Intercept) 0.49348 0.70248 order (Intercept) 0.00000 0.00000 Number of obs: 1116, groups: family:order, 43; order, 9 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.6034 0.4227 1.427 0.15346 seasonality2 -1.1421 0.3453 -3.308 0.00094 *** woodyness2 0.5113 0.2559 1.998 0.04572 * On 5 Aug 2010, at 13:51, Ben Bolker wrote: Chris Mcowen <chrismco...@...> writes: > > Hi Philip, > > Thanks very much for this, i was completely unaware. I have read various papers using lmer to calculate the > AIC statistic and none have mentioned this? > > I have just run through a random section of my models with this correction, however the AIC / BIC values are > the same with the REML=F in and out? > > Chris Try REML=FALSE instead ... ? (You may have 'F' set to a value in your workspace.) Otherwise I would find it very odd that the results are identical. _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology