[R-sig-ME] Linear mixed effect model

[Ben Bolker]

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