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
Best,
Manuel
On 18/03/2011 06:51 a.m., Ben Bolker wrote:
> On 11-03-18 08:19 AM, Manuel Spínola wrote:
>> Dear list members,
>>
>> I am trying to fit a linear mixed model using the following variables::
>>
>> Response variable:
>>
>> Swiftness.2 (This is the time it took for the otter to first approach
>> the lure. The time ranges from 1 second (in which case the otter
>> approached the lure almost immediately) to 600 seconds (10 minutes).
>>
>> Explanatory variables:
>> 1) Subject (this is the individual otter -- each otter is measured for
>> response to each lure, so it is a repeated measure on the individual);
>> 2) sex;
>> 3) facility size (small, med, large);
>> 4) lure type (there were 6).
>>
>> I would like to see if the response variable is influenced by the
>> explanatory variables including Subject like a "repeated measure" term
>> (same animal expose to different lures).
>>
>> I am fitting the model:
>>
>> otter$Facility.Size = factor(otter$Facility.Size)
>> otter$Sex = factor(otter$Sex)
>>
>> mod1 = lmer(Swiftness.1 ~ Lure + Sex + Facility.Size + (1|Subject), data
>> = otter)
>> summary(mod1)
>>
>> > mod1 = lmer(Swiftness.1 ~ Lure + Sex + Facility.Size + (1|Subject),
>> data = otter)
>> > summary(mod1)
>> Linear mixed model fit by REML
>> Formula: Swiftness.1 ~ Lure + Sex + Facility.Size + (1 | Subject)
>> Data: otter
>> AIC BIC logLik deviance REMLdev
>> 1277 1295 -631.3 1302 1263
>> Random effects:
>> Groups Name Variance Std.Dev.
>> Subject (Intercept) 0 0.00
>> Residual 21558 146.83
>> Number of obs: 102, groups: Subject, 17
>>
>> Fixed effects:
>> Estimate Std. Error t value
>> (Intercept) 92.883 44.711 2.077
>> Lure -6.286 8.513 -0.738
>> Sex1 -3.266 29.199 -0.112
>> Facility.Size2 24.174 37.628 0.642
>> Facility.Size3 58.528 38.692 1.513
>>
>> Correlation of Fixed Effects:
>> (Intr) Lure Sex1 Fcl.S2
>> Lure -0.666
>> Sex1 -0.327 0.000
>> Facilty.Sz2 -0.516 0.000 -0.055
>> Facilty.Sz3 -0.519 0.000 0.000 0.617
>>
>>
>> Is the model a plausible model and is it well parameterized?
> Plausible, yes, except that you have apparently failed to
> transform Lure into a factor -- as it stands, lmer is treating
> it as a continuous covariate.
> Effects seem quite small.
> I would worry a little about your distribution, because I would guess
> that elapsed times are likely to be skewed. Have you looked at the
> residuals/thought about log-transforming?
> You are getting zero variance for the random effect (and a huge
> residual variance), which suggests a general lack of power.
>
> Ben Bolker
>
>
>
--
*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
Institutional website: ICOMVIS
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