[R-sig-ME] MCMCglmm with missing values

[Jarrod]

Hi Monica,

You don’t need to split male and fitness up into separate columns. Just have a column for Fitness and use interactions between sex and trait to define the model you want. Your current specification is ill-defined because you’re trying to model a covariance between male and female fitness at the residual level.

Cheers,

Jarrod


> On 16 Mar 2020, at 13:24, Monica Anderson Berdal <m.anderson.berdal using gmail.com> wrote:
>
> Sorry, here is a plain text version with CSV data.
>
> I’m trying to run a MCMCglmm where two of the response variables and
> three of the fixed effects are sex specific. Each line represents one
> individual, and the data looks like this:
>
> Sex,Fitness_M,Fitness_F,Age_M,Age_F,Mate_mass,Behaviour,Mass,Temperature,Line
>
> M,743,NA,140,NA,NA,69,229,21,X
>
> M,515,NA,188,NA,NA,50,220,19,X
>
> M,390,NA,231,NA,NA,50,109,17,Y
>
> M,266,NA,96,NA,NA,39,113,19,Y
>
> M,323,NA,105,NA,NA,55,171,25,Y
>
> F,NA,112,NA,74,205,67,247,20,Y
>
> F,NA,107,NA,74,163,60,139,26,Z
>
> F,NA,8,NA,53,193,71,118,24,Z
>
> F,NA,207,NA,55,74,37,219,21,Z
>
> F,NA,300,NA,56,160,68,261,18,Z
>
> All individuals have a behaviour mass recorded, and only the sex
> specific variables have NAs. I’ve been using the at.level function to
> control for these missing values, but still get the error: Missing
> values in the fixed predictors
>
> cricketMCMC <- MCMCglmm(cbind(Fitness_F, Fitness_M, Behaviour, Mass)~
>
>                          at.level(trait, 1):(Age_F + Mate_mass) +
>
>                          at.level(trait, 2):(Age_M) +
>
>                          at.level(trait, 3):(Sex + Temperature) +
>
>                          at.level(trait, 4):(Sex + Temperature),
>
>                        random=~us(trait):Line,rcov=~us(trait):units,
>
>                        family=rep("gaussian",4), nitt=260000,
> thin=200, burnin=60000,
>
>                        verbose=FALSE, prior=prior.4t, pr=TRUE, data=Data)
>
>
>
> To avoid the issues of missing values I added mean values for Age_F
> and Mate_mass for the males and Age_M for the females, while still
> using the at.level function. The idea was to overcome the problem with
> the missing values while also ignoring the mean values for the sex
> specific traits. This model ran without problems, and to see if the
> model ran properly, I compared it to a second model where I added a
> random value instead of mean values. I used set.seed() before running
> both models, but the outputs are not the same, which means that the
> added values are still affecting the results.
>
> How can I avoid the problems of NAs when running an MCMCglmm on a data
> set with this structure?
>
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