1. This has nothing to do with R. It's your lack of understanding of linear
models issues. See ?contrasts and ?contrast for the specific, but I doubt
that you will understand how these fit in with the underlying statistical
issues (and I would be delighted to be wrong). So, in order of (my
)preference, you should try:

a) Consult a local statistician;
b) Post on r-sig-mixed-models
c) Post on a statistical advice list like stats.stackexchange.com

.
Cheers,
Bert


On Sat, Jun 30, 2012 at 9:44 AM, Andy Robertson <ar313@exeter.ac.uk> wrote:

> Dear R users,
>
>
>
> I am using lmer combined with AIC model selection and averaging (in the
> MuMIn package) to try and assess how isotope values (which indicate diet)
> vary within a population of animals.
>
>
>
> I have multiple measures from individuals (variable 'Tattoo') and multiple
> individuals within social groups within 4 locations (A, B, C ,D) crucially
> I
> am interested if there are differences between sexes and age classes
> (variable AGECAT2) and whether this differs with location.
>
> However, whether or not I get a significant sex:location interaction
> depends
> on which location is my reference level and I cannot understand why this is
> the case. It seems to be due to the fact that the standard error associated
> with my interactions varies depending on which level is the reference.
>
>
>
> Any help or advice would be appreciated,
>
>
>
> Andrew Robertson
>
>
>
> Below is the example code of what I am doing and an example of the model
> summary and model averaging results with location A as the ref level or
> location B.
>
>
>
> if A is the reference level...
>
>
>
> #full model
>
>
> Amodel<-lmer(d15N~(AGECAT2+Sex+Location1+AGECAT2:Location1+Sex:Location1+AGE
> CAT2:Sex+(1|Year)+(1|Location1/Socialgroup/Tattoo)), REML=FALSE,
> data=nocubs)
>
>
>
> #standardise model
>
> Amodels<-standardize(Amodel, standardize.y=FALSE)
>
>
>
> #dredge models
>
> summary(model.avg(get.models(Adredge,cumsum(weight)<0.95)))
>
>
>
> Then the average model coefficients indicate no sex by location interaction
>
>
> Component models:
>       df  logLik    AICc Delta Weight
> 235   13 -765.33 1557.28  0.00   0.68
> 1235  15 -764.55 1559.91  2.63   0.18
> 3      9 -771.64 1561.57  4.29   0.08
> 12345 17 -763.67 1562.37  5.09   0.05
>
> Term codes:
>         AGECAT2           c.Sex       Location1   AGECAT2:c.Sex
> c.Sex:Location1
>               1               2               3               4
> 5
>
> Model-averaged coefficients:
>                        Estimate Std. Error z value Pr(>|z|)
> (Intercept)            8.673592   0.474524  18.279   <2e-16 ***
> c.Sex                  0.095375   0.452065   0.211    0.833
> Location1B            -3.972882   0.556575   7.138   <2e-16 ***
> Location1C            -3.633331   0.531858   6.831   <2e-16 ***
> Location1D            -3.348665   0.539143   6.211   <2e-16 ***
> c.Sex:Location1B      -0.372653   0.513492   0.726    0.468
> c.Sex:Location1C       0.428299   0.511254   0.838    0.402
> c.Sex:Location1D      -0.757582   0.512586   1.478    0.139
> AGECAT2OLD            -0.179772   0.150842   1.192    0.233
> AGECAT2YEARLING       -0.009596   0.132328   0.073    0.942
> AGECAT2OLD:c.Sex       0.045963   0.296471   0.155    0.877
> AGECAT2YEARLING:c.Sex -0.323985   0.268919   1.205    0.228
> ---
>
>
> And the full model summary looks like this..
>
>
>
>
>
> Linear mixed model fit by maximum likelihood
>
> Formula: d15N ~ (AGECAT2 + Sex + Location1 + AGECAT2:Location1 +
> Sex:Location1 +      AGECAT2:Sex + (1 | Year) + (1 |
> Location1/Socialgroup/Tattoo))
>
>    Data: nocubs
>
>   AIC  BIC logLik deviance REMLdev
>
> 1568 1670 -761.1     1522    1534
>
> Random effects:
>
> Groups                         Name        Variance Std.Dev.
>
> Tattoo:(Socialgroup:Location1) (Intercept) 0.35500  0.59582
>
>  Socialgroup:Location1          (Intercept) 0.35620  0.59682
>
>  Location1                      (Intercept) 0.00000  0.00000
>
>  Year                           (Intercept) 0.00000  0.00000
>
>  Residual                                   0.49584  0.70416
>
> Number of obs: 608, groups: Tattoo:(Socialgroup:Location1), 132;
> Socialgroup:Location1, 22; Location1, 4; Year, 2
>
>
>
> Fixed effects:
>
>                            Estimate Std. Error t value
>
> (Intercept)                 8.83179    0.52961  16.676
>
> AGECAT2OLD                 -0.44101    0.41081  -1.074
>
> AGECAT2YEARLING             0.01805    0.38698   0.047
>
> SexMale                    -0.11346    0.51239  -0.221
>
> Location1B                 -3.97880    0.63063  -6.309
>
> Location1C                 -4.04816    0.60404  -6.702
>
> Location1D                 -3.36389    0.63304  -5.314
>
> AGECAT2OLD:Location1B       0.44198    0.54751   0.807
>
> AGECAT2YEARLING:Location1B -0.22134    0.52784  -0.419
>
> AGECAT2OLD:Location1C       0.20684    0.50157   0.412
>
> AGECAT2YEARLING:Location1C  0.24132    0.47770   0.505
>
> AGECAT2OLD:Location1D       0.53653    0.52778   1.017
>
> AGECAT2YEARLING:Location1D  0.51755    0.51038   1.014
>
> SexMale:Location1B         -0.02442    0.57546  -0.042
>
> SexMale:Location1C          0.74680    0.58128   1.285
>
> SexMale:Location1D         -0.41800    0.59505  -0.702
>
> AGECAT2OLD:SexMale         -0.08907    0.32513  -0.274
>
> AGECAT2YEARLING:SexMale    -0.40146    0.30409  -1.320
>
>
>
>
>
> If location B is the reference level then the average model coefficients
> indicate an age by sex interaction in location C.
>
>
>
> Component models:
>       df  logLik    AICc Delta Weight
> 235   13 -765.33 1557.28  0.00   0.68
> 1235  15 -764.55 1559.91  2.63   0.18
> 3      9 -771.64 1561.57  4.29   0.08
> 12345 17 -763.67 1562.37  5.09   0.05
>
> Term codes:
>         AGECAT2           c.Sex       Location2   AGECAT2:c.Sex
> c.Sex:Location2
>               1               2               3               4
> 5
>
> Model-averaged coefficients:
>                        Estimate Std. Error z value Pr(>|z|)
> (Intercept)            4.700710   0.294275  15.974   <2e-16 ***
> c.Sex                 -0.277278   0.248093   1.118   0.2637
> Location2A             3.972882   0.556575   7.138   <2e-16 ***
> Location2C             0.339551   0.379873   0.894   0.3714
> Location2D             0.624217   0.390063   1.600   0.1095
> c.Sex:Location2A       0.372653   0.513492   0.726   0.4680
> c.Sex:Location2C       0.800952   0.345898   2.316   0.0206 *
> c.Sex:Location2D      -0.384929   0.346832   1.110   0.2671
> AGECAT2OLD            -0.179772   0.150842   1.192   0.2333
> AGECAT2YEARLING       -0.009596   0.132328   0.073   0.9422
> AGECAT2OLD:c.Sex       0.045963   0.296471   0.155   0.8768
> AGECAT2YEARLING:c.Sex -0.323985   0.268919   1.205   0.2283
>
> And the full model summary looks like this..
>
> ---
>
> Linear mixed model fit by maximum likelihood
>
> Formula: d15N ~ (AGECAT2 + Sex + Location2 + AGECAT2:Location2 +
> Sex:Location2 +      AGECAT2:Sex + (1 | Year) + (1 |
> Location2/Socialgroup/Tattoo))
>
>    Data: nocubs
>
>   AIC  BIC logLik deviance REMLdev
>
> 1568 1670 -761.1     1522    1534
>
> Random effects:
>
> Groups                         Name        Variance Std.Dev.
>
> Tattoo:(Socialgroup:Location2) (Intercept) 0.35500  0.59582
>
>  Socialgroup:Location2          (Intercept) 0.35618  0.59681
>
>  Location2                      (Intercept) 0.00000  0.00000
>
>  Year                           (Intercept) 0.00000  0.00000
>
>  Residual                                   0.49584  0.70416
>
> Number of obs: 608, groups: Tattoo:(Socialgroup:Location2), 132;
> Socialgroup:Location2, 22; Location2, 4; Year, 2
>
>
>
> Fixed effects:
>
>                             Estimate Std. Error t value
>
> (Intercept)                 4.852982   0.342364  14.175
>
> AGECAT2OLD                  0.000986   0.361951   0.003
>
> AGECAT2YEARLING            -0.203275   0.358971  -0.566
>
> SexMale                    -0.137881   0.261931  -0.526
>
> Location2A                  3.978806   0.630652   6.309
>
> Location2C                 -0.069353   0.444658  -0.156
>
> Location2D                  0.614917   0.479262   1.283
>
> AGECAT2OLD:Location2A      -0.441995   0.547521  -0.807
>
> AGECAT2YEARLING:Location2A  0.221330   0.527840   0.419
>
> AGECAT2OLD:Location2C      -0.235146   0.434839  -0.541
>
> AGECAT2YEARLING:Location2C  0.462657   0.357815   1.293
>
> AGECAT2OLD:Location2D       0.094536   0.442264   0.214
>
> AGECAT2YEARLING:Location2D  0.738882   0.375638   1.967
>
> SexMale:Location2A          0.024425   0.575468   0.042
>
> SexMale:Location2C          0.771228   0.351708   2.193
>
> SexMale:Location2D         -0.393576   0.364486  -1.080
>
> AGECAT2OLD:SexMale         -0.089071   0.325140  -0.274
>
> AGECAT2YEARLING:SexMale    -0.401467   0.304098  -1.320
>
>
>
> The results are also different if location C or D are the reference levels
>
>
>
>
>
> Andrew Robertson
> PhD student
> Centre for Ecology and Conservation
> University of Exeter, Cornwall Campus
> Tremough, Cornwall. TR10 9EZ
> UK
>
> Tel: 01326 371852
> Email:  <mailto:ar313@exeter.ac.uk> ar313@exeter.ac.uk
> Web page:
> <http://biosciences.exeter.ac.uk/staff/postgradresearch/andrewrobertson/>
> http://biosciences.exeter.ac.uk/staff/postgradresearch/andrewrobertson/
>
> LinkedIn:  <http://uk.linkedin.com/pub/andrew-robertson/39/91a/504>
> http://uk.linkedin.com/pub/andrew-robertson/39/91a/504
>
>
>
>
>
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>



-- 

Bert Gunter
Genentech Nonclinical Biostatistics

Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm

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