`"ape"`

(Paradis et al 2004)

`"MCMCglmm"`

(Hadfield, 2010a)

**Count data** (`"data_pois_missing.txt"`

), Data frame containing the Poisson data with missing values

**Phylogeny** (`"phylo.nex"`

), Phylogeny file (NEXUS file)

We will use the same data set as in the OPM section 11.4,
but this time, we are missing one half of trait data `phen_pois` (100
out of 200 species).
However, we have a complete data set for the cofactor, which, in this case, was
related to missingness;
we deleted the 50 trait values that had the 50 lowest values for the cofactor.
Therefore, missing data in this
data set is MAR. Let's look at the data:

library(ape) library(MCMCglmm) phylo<-read.nexus("phylo.nex") data<-read.table("data_pois_missing.txt",header=TRUE) head(data)

## phen_pois cofactor phylo ## 1 NA 7.8703 sp_1 ## 2 NA 3.4691 sp_2 ## 3 NA 2.5479 sp_3 ## 4 14 18.2287 sp_4 ## 5 NA 2.5303 sp_5 ## 6 NA 0.5146 sp_6

inv.phylo<-inverseA(phylo,nodes="TIPS",scale=TRUE) prior<-list(G=list(G1=list(V=1,nu=0.02)),R=list(V=1,nu=0.02)) model_missing<-MCMCglmm(phen_pois~cofactor,random=~phylo, family="poisson",ginverse=list(phylo=inv.phylo$Ainv), prior=prior,data=data,nitt=5000000,burnin=1000,thin=500)

Warning message: In MCMCglmm(phen_pois ~ cofactor, random = ~phylo, family = "poisson", : some combinations in phylo do not exist and 198 missing records have been generated

summary(model_missing)

## ## Iterations = 1001:4999501 ## Thinning interval = 500 ## Sample size = 9998 ## ## DIC: 615.5 ## ## G-structure: ~animal ## ## post.mean l-95% CI u-95% CI eff.samp ## animal 0.0467 0.00239 0.128 8679 ## ## R-structure: ~units ## ## post.mean l-95% CI u-95% CI eff.samp ## units 0.0399 0.00281 0.0923 8954 ## ## Location effects: phen_pois ~ cofactor ## ## post.mean l-95% CI u-95% CI eff.samp pMCMC ## (Intercept) -2.246 -2.723 -1.775 9050 <1e-04 *** ## cofactor 0.260 0.234 0.287 9432 <1e-04 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1