[R-sig-eco] AIC / BIC vs P-Values in lmer
Chris Mcowen
chrismcowen at gmail.com
Thu Aug 5 15:19:07 CEST 2010
> They are described as
> “nearly” interchangeable because the ‘REML’ argument only applies
> to calls to ‘lmer’ and the ‘nAGQ’ argument only applies to calls
> to ‘glmer’
I am using lmer?
Thanks
Chris
On 5 Aug 2010, at 14:16, Manuel Morales wrote:
REML does not apply for glmer fits:
Details:
The ‘lmer’ and ‘glmer’ functions are nearly interchangeable. If
‘lmer’ is called with a non-default ‘family’ argument the call is
replaced by a call to ‘glmer’ with the current arguments. If
‘glmer’ is called with the default ‘family’, namely the ‘gaussian’
family with the identity link, then the call is replaced by a call
to ‘lmer’ with the current arguments. (They are described as
“nearly” interchangeable because the ‘REML’ argument only applies
to calls to ‘lmer’ and the ‘nAGQ’ argument only applies to calls
to ‘glmer’.)
On Thu, 2010-08-05 at 14:04 +0100, Chris Mcowen wrote:
> 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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