[R-sig-ME] MCMCglmm phylogenetically controlled categorical R structure and priors help

[ben hogan]

Hello,


I have collected categorical data about the colouration of a number of bird species (4 levels), and am attempting to see if colouration is correlated with the proportions (as percentage) of different prey types, and the birds' length. There is only one observation of each factor for each bird species, i.e. one row in the data frame.


I am having difficulty in understanding the outcome of the R-structure, and how to properly define the priors. I don't have any specific predictions about priors, and I believe the code I have used is supposed to generate flat/uninformed ones, but am not sure if that it in fact the case (very new to the subject). I am not sure if both pedigree and ginverse are necessary, I get the similar output without pedigree.


As it stands my model and priors for a model on F_Wing2 are;


k <- length(levels(Data$F_Wing2))
IJ <- (1/k) * (diag(k-1) + matrix(1, k-1, k-1))

prior.phyl = list(R = list(V = IJ, nu = 0),G = list( G1 = list(V = IJ, n = k-1 , nu = 0) ) )


Ainv<-inverseA(Tree, scale=FALSE, nodes="TIPS")$Ainv

myMCMC.phyl<- MCMCglmm(F_Wing2 ~ Birds+Reptiles+Amphibians+Fish+Mammals+as.numeric(Length),
random=~us(trait):species,
rcov = ~us(trait):units,
pedigree=Tree,
scale=FALSE,
ginverse = list(species=Ainv),
data = Data, family="categorical",
prior=prior.phyl,
nitt=10000,
thin=25,
burnin=2000)

This runs, and nitt etc are artificially low for testing purposes. The outcome is something like this;

DIC: 160.7702

 G-structure:  ~us(trait):species

                                                    post.mean l-95% CI u-95% CI eff.samp
traitF_Wing2.Bicolour:traitF_Wing2.Bicolour.species    112.05    2.267    237.3    2.048
traitF_Wing2.Mottled:traitF_Wing2.Bicolour.species      75.29    1.450    160.9    4.397
traitF_Wing2.Plain:traitF_Wing2.Bicolour.species       125.34    3.015    268.0    2.707
traitF_Wing2.Bicolour:traitF_Wing2.Mottled.species      75.29    1.450    160.9    4.397
traitF_Wing2.Mottled:traitF_Wing2.Mottled.species       52.12    1.345    123.3    7.140
traitF_Wing2.Plain:traitF_Wing2.Mottled.species         85.01    2.861    188.4    4.175
traitF_Wing2.Bicolour:traitF_Wing2.Plain.species       125.34    3.015    268.0    2.707
traitF_Wing2.Mottled:traitF_Wing2.Plain.species         85.01    2.861    188.4    4.175
traitF_Wing2.Plain:traitF_Wing2.Plain.species          143.20    5.880    310.2    3.252

 R-structure:  ~us(trait):units

                                                  post.mean l-95% CI u-95% CI eff.samp
traitF_Wing2.Bicolour:traitF_Wing2.Bicolour.units      0.50     0.50     0.50        0
traitF_Wing2.Mottled:traitF_Wing2.Bicolour.units       0.25     0.25     0.25        0
traitF_Wing2.Plain:traitF_Wing2.Bicolour.units         0.25     0.25     0.25        0
traitF_Wing2.Bicolour:traitF_Wing2.Mottled.units       0.25     0.25     0.25        0
traitF_Wing2.Mottled:traitF_Wing2.Mottled.units        0.50     0.50     0.50        0
traitF_Wing2.Plain:traitF_Wing2.Mottled.units          0.25     0.25     0.25        0
traitF_Wing2.Bicolour:traitF_Wing2.Plain.units         0.25     0.25     0.25        0
traitF_Wing2.Mottled:traitF_Wing2.Plain.units          0.25     0.25     0.25        0
traitF_Wing2.Plain:traitF_Wing2.Plain.units            0.50     0.50     0.50        0

 Location effects: F_Wing2 ~ Birds + Reptiles + Amphibians + Fish + Mammals + as.numeric(Length)

                    post.mean   l-95% CI   u-95% CI eff.samp  pMCMC
(Intercept)        -10.128704 -19.972170  -0.276258   14.864 0.0187 *
Birds               -0.058857  -0.116441  -0.005308   24.074 0.0312 *
Reptiles            -0.059507  -0.148332   0.011748    6.368 0.1313
Amphibians          -0.098699  -0.336341   0.078327   12.190 0.3438
Fish                -0.104092  -0.226801  -0.010597   11.712 0.0563 .
Mammals             -0.006519  -0.062639   0.044000   17.301 0.7250
as.numeric(Length)   0.097417  -0.013097   0.232505    9.862 0.1187
---

As you can see, the R-Structure seems not to have been affected by the running of the model, and effective sample sizes are 0. 1) I am not sure why this is, and 2) the model doesn't run unless rcov = ~us(trait):units, but I do not understand what "units" refers to.

Any help greatly appreciated!

Best,
Ben Hogan





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