[R-sig-ME] Unrealistic coefficient values from an MCMCglmm mixed model

[Ronan James Osullivan]

Dear forum,

I am struggling with the interpretation of the coefficients from a glmm
implemented using MCMCglmm. My data set has lifetime reproductive success
(LRS) for individual fish and associated climatic variables (NAO and
Temperature) are indexed to each fish. I also know the genetic type of each
fish (A or B). In total, I have 2938 observations with 1321 A fish and 1671
B fish.

I ran the following model:

model<- MCMCglmm(LRS~GeneticType*NAO+
                             GeneticType *Temp,
                          random = ~Year_of_Spawning,
                          family = "poisson",
                          data = data,
                          verbose = TRUE,
                          nitt = 1010000, burnin = 1000, thin = 1000)

Which gave the following summary:

 G-structure:  ~Year_of_Spawning

                                 post.mean     l-95% CI        u-95% CI
  eff.samp
Year_of_Spawning       0.792            0.1521            1.934
1111

 R-structure:  ~units

                              post.mean         l-95% CI        u-95% CI
 eff.samp
       units                  1.257                 1.029
1.488         1447

 Location effects: LRS ~  GeneticType *NAO + GeneticType * Temp-1

                                     post.mean      l-95% CI      u-95% CI
   eff.samp    pMCMC
GeneticTypeA                 5.5510        -4.5345        15.9627
 1009.0      0.2577
GeneticTypeB                -2.8334      -10.6020         8.0720
1009.0     0.4916
NAO                                0.6995        -0.6520         1.9512
        918.3     0.2220
Temp                              -8.2807      -18.7522         1.8865
    1009.0     0.0991 .
GeneticTypeB:NAO       -0.7729         -1.2302       -0.3119
 1009.0   <0.001 ***
GeneticTypeB:Temp       9.8697          4.7849        15.4317
 1009.0   <0.001 ***

My issue is that the predicted LRS values for Genetic Type A are far too
high. The intercept for A fish is 5.551, and exp(5.551) = expected mean for
H fish when Temp=0 and NAO = 0 is 244.7. When I solve for LRS for Type A
fish at a given NAO or temperature (holding the other one constant), I get
incredibly high values.

The predicted LRS for Genetic Type B is exp(-2.8334)=0.05881255 which is
far more realistic for my study system

Am I somehow mis-specifying the model or mis-calculating LRS with respect
to each genetic type?

Cheers,
Ronan



-- 
Ronan O'Sullivan | Ph.D student | School of Biological, Earth and
Environmental Sciences, University College Cork, Ireland |
http://fisheye.ucc.ie/toms-team/  <http://fisheye.ucc.ie/toms-team/>

Irish Ecological Association - Ordinary Committee Member

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