[R-sig-eco] AIC / BIC vs P-Values in lmer
Ben Bolker
bbolker at gmail.com
Thu Aug 5 16:13:25 CEST 2010
Thanks (forehead slap -- I knew that but it escaped me -- Manuel
Morales also pointed this out, off-list).
Isn't the difference between
(1|order/family)
and
(1|family)
that the former fits two variance terms, one for differences among orders and
one for families (implicitly, within orders)? I think they're
different (it should
be very easy to tell from the model output -- although if the data are scarce
it could be that among-order variance is estimated to be effectively zero,
in which case the results wouldn't differ much).
On Thu, Aug 5, 2010 at 9:23 AM, Crowe, Andrew <a.crowe at lancaster.ac.uk> 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.
>
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