AIC / BIC vs P-Values in lmer
Hi Andrew, Thanks very much for this, however, does this mean that the problems with interpreting the AIC value as explained by Phillip Dixon still apply?
1) the AIC calculated from the reml lnL only informs you about the fit of the random effects model. 2) the reml AIC can only be compared between models with the same fixed effects. Otherwise, the lnL is being calculated from different data (because different residuals with different X's).
If so is there a work around using binomial data in lmer? Thanks Chris
On 5 Aug 2010, at 14:22, Crowe, Andrew wrote:
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-bounces at r-project.org on behalf of Chris Mcowen Sent: Thu 05/08/2010 2:04 PM To: Ben Bolker Cc: r-sig-ecology at 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 <chrismcowen at ...> 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 at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology _______________________________________________ R-sig-ecology mailing list R-sig-ecology at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology