Hiya,

I'm fairly new to MuMIn (and plotting predicted values in R, for that
matter) and I have quite a basic question:

I want to plot model-averaged estimates with their 95% CI's for the
supported interaction (Infection x ecs). I used the following data:


> head(summ12b)
  ld sum12  Infection       ecs
1 13  78.0   Infected Enlarged
2 15 134.5 Uninfected Enlarged
3 14  91.5   Infected   Control
4 16  62.0   Infected  Reduced
5 17  93.6   Infected   Control
6 15 104.6 Uninfected   Control



>s12b<-lm(sum12~(Infection+ecs)^2+ld,data=summ12b)
>ss12b<-dredge(s12b,trace = TRUE, rank = "AICc")
>top.ss12b<- get.models(ss12b, subset = delta<2)
>ad<-summary(model.avg(top.ss12b))



and here's what I've got:

>ad

Call:
model.avg.default(object = top.ss12b)

Component models:
     df  logLik   AICc Delta Weight
1234  8 -112.95 249.10  0.00   0.47
124   7 -115.31 249.95  0.86   0.31
1     4 -120.44 250.55  1.45   0.23

Term codes:
          ecs     Infection            ld ecs:Infection
            1             2             3             4

Model-averaged coefficients:
                                 Estimate Std. Error Adjusted SE z value
Pr(>|z|)
(Intercept)                       112.737     30.847      31.505   3.578
0.000346 ***
ecsEnlarged                        23.817     13.376      13.798   1.726
0.084324 .
ecsReduced                        -10.597      8.352       8.740   1.212
0.225364
InfectionUninfected                 7.493      9.381       9.893   0.757
0.448793
ld                                 -3.477      1.763       1.865   1.864
0.062252 .
ecsEnlarged :InfectionUninfected   23.308     14.740      15.510   1.503
0.132892
ecsReduced :InfectionUninfected   -22.112     12.589      13.307   1.662
0.096572 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Full model-averaged coefficients (with shrinkage):
 (Intercept) ecsEnlarged  ecsReduced  InfectionUninfected       ld
ecsEnlarged :InfectionUninfected
    112.7373      23.8169    -10.5966              5.7952
-1.6285                          18.0261
 ecsReduced :InfectionUninfected
                        -17.1008

Relative variable importance:
          ecs     Infection ecs:Infection            ld
         1.00          0.77          0.77          0.47


I know that I can obtain 95% Ci's by using ad$avg.model, however, how to
derive the estimates and CI's for ALL the groups and their factor levels
included in the interaction?
Thanks for your help!

cheers,
Kasia

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