Linear mixed effect model

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?

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

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?