how to extract the BIC value

On Mon, May 17, 2010 at 5:54 AM, Andy Fugard (Work)

Greetings,

Assuming you're using lmer, here's an example which does what you need:

(fm1 ? ?<- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))

Linear mixed model fit by REML
Formula: Reaction ~ Days + (Days | Subject)
? Data: sleepstudy
?AIC ?BIC logLik deviance REMLdev
?1756 1775 -871.8 ? ? 1752 ? ?1744
Random effects:
?Groups ? Name ? ? ? ?Variance Std.Dev. Corr
?Subject ?(Intercept) 612.092 ?24.7405
? ? ? ? ?Days ? ? ? ? 35.072 ? 5.9221 ?0.066
?Residual ? ? ? ? ? ? 654.941 ?25.5918
Number of obs: 180, groups: Subject, 18

Fixed effects:
? ? ? ? ? ?Estimate Std. Error t value
(Intercept) ?251.405 ? ? ?6.825 ? 36.84
Days ? ? ? ? ?10.467 ? ? ?1.546 ? ?6.77

Correlation of Fixed Effects:
? ? (Intr)
Days -0.138

(fm1fit <- summary(fm1)@AICtab)

? ? ?AIC ? ? ?BIC ? ?logLik deviance ?REMLdev
?1755.628 1774.786 -871.8141 1751.986 1743.628

fm1fit$BIC

[1] 1774.786

That's one way of doing it but it relies on a particular
representation of the object returned by summary, and that is subject
to change.

I had thought that it would work to use

BIC(logLik(fm1))

but that doesn't because the BIC function is imported from the nlme
package but not later exported.  The situation is rather tricky - at
one point I defined a generic for BIC in the lme4 package but that led
to conflicts when multiple packages defined different versions.  The
order in which the packages were loaded became important in
determining which version was used.

We agreed to use the generic from the nlme package, which is what is
now done.  However, I don't want to make the entire nlme package
visible when you have loaded lme4 because of resulting conflicts.

I can get the result as

(fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))

Linear mixed model fit by REML
Formula: Reaction ~ Days + (Days | Subject)
   Data: sleepstudy
  AIC  BIC logLik deviance REMLdev
 1756 1775 -871.8     1752    1744
Random effects:
 Groups   Name        Variance Std.Dev. Corr
 Subject  (Intercept) 612.090  24.7405
          Days         35.072   5.9221  0.066
 Residual             654.941  25.5918
Number of obs: 180, groups: Subject, 18

Fixed effects:
            Estimate Std. Error t value
(Intercept)  251.405      6.825   36.84
Days          10.467      1.546    6.77

Correlation of Fixed Effects:
     (Intr)
Days -0.138

nlme:::BIC(logLik(fm1))

REML
1774.786

but that is unintuitive.  I am not sure what the best approach is.
Perhaps Martin (or anyone else who knows namespace intricacies) can
suggest something.

Tahira Jamil wrote:

Hi
I can extract the AIC value of a model like this

AIC(logLik(fm0)

How can I extract the BIC value if I need!

Cheers
Tahira
Biometris
Wageningen University

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--
Andy Fugard, Postdoctoral researcher, ESF LogICCC project
"Modeling human inference within the framework of probability logic"
Department of Psychology, University of Salzburg, Austria
http://www.andyfugard.info

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