Thank you very much Ben,

I decided to keep "otter" as a random factor:

modA = lmer(Swiftness.1 ~ Lure + Sex + Facility.Size + (1|Subject), REML 
= F, data = otter)
summary(modA)

modB = lmer(Swiftness.1 ~ (1|Subject), REML = F, data = otter)
summary(modB)

modC = lmer(Swiftness.1 ~ Lure + (1|Subject), REML = F, data = otter)
summary(modC)

 > AICctab(modA, modB, modC, weights = T, delta = TRUE, base = T, sort = 
TRUE, nobs = 17)
      AICc   df dAICc  weight
modB 1313.1 3     0.0 1
modC 1336.3 8    23.2 <0.001
modA 1374.5 11   61.4 <0.001

Output for best model:

 > summary(modB)
Linear mixed model fit by maximum likelihood
Formula: Swiftness.1 ~ (1 | Subject)
    Data: otter
   AIC  BIC logLik deviance REMLdev
  1311 1319 -652.6     1305    1298
Random effects:
  Groups   Name        Variance Std.Dev.
  Subject  (Intercept)     0      0.00
  Residual             21133    145.37
Number of obs: 102, groups: Subject, 17

Fixed effects:
             Estimate Std. Error t value
(Intercept)    99.76      14.39   6.931


Is it fair to say that there is no effect of any of the factors?
Did you say that the variance 0 in the random effect output is low power?

Best,

Manuel



On 19/03/2011 09:07 a.m., Ben Bolker wrote:
> On 11-03-19 10:47 AM, Manuel Spínola wrote:
>> Hi Ben and other list members,
>>
>> I looked at the residuals and log transforming the gave me
>> heteroscedasticity, so I don't know if I need to transform.
>>
>> Is statistically appropriate to fit different models, lienear, gls, lme
>> and compare them with AIC?
>>
>>   mod1 = lm(Swiftness.1 ~ Lure + Sex + Facility.Size, data = otter)
>> mod2 = gls(Swiftness.1 ~ Lure + Sex + Facility.Size, data = otter)
>> mod3 = gls(Swiftness.1 ~ 1, data = otter)
>> mod4 =  lme(Swiftness.1 ~ Lure + Sex + Facility.Size, random =
>> ~1|Subject, data = otter)
>>
>>> AICctab(mod1, mod2, mod3, mod4, weights = T, delta = TRUE, base = T,
>> sort = TRUE, nobs = 17)
>>                 AICc       df    dAICc   weight
>> mod2    1276.4   10    0.0        1
>> mod4   1294.5   11   18.1<0.001
>> mod3   1302.9     2    26.6<0.001
>> mod1   1356.3  10     80.0<0.001
>>
>     A few thoughts:
>
> * you can in principle compare various models (including those
> with/without random effects), but it is a crude approximation for
> several reasons (boundary issues with random effects, marginal vs
> conditional AIC, etc. -- see<http://glmm.wikidot.com/faq>  for more
> discussion).
>
> * Take a look at very recent discussions on this list about comparing lm
> vs gls vs lme; in particular make sure you have REML=TRUE/FALSE set
> appropriately.  As you have done it, the fits may not be comparable.
>
> * I think you should retain the random effect of 'otter' in any case
> because it is a natural part of the experimental design (although I
> think that if you correctly set REML=FALSE you will get identical
> likelihoods between gls and lme, and gls will appear better because it
> is missing a random-effect variance parameter)
>
>


-- 
*Manuel Spínola, Ph.D.*
Instituto Internacional en Conservación y Manejo de Vida Silvestre
Universidad Nacional
Apartado 1350-3000
Heredia
COSTA RICA
mspinola@una.ac.cr
mspinola10@gmail.com
Teléfono: (506) 2277-3598
Fax: (506) 2237-7036
Personal website: Lobito de río 
<https://sites.google.com/site/lobitoderio/>
Institutional website: ICOMVIS <http://www.icomvis.una.ac.cr/>

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