[R-sig-ME] making predictions with MCMCglmm

[Tom Houslay]

Hi YA,

In your new data frame you just need to add a column for the response
variable as well (set it to 0 or similar). You may have additional issues
but that should get you over that first hurdle.

Speaking of which, I've found when predicting from MCMCglmm that it doesn't
like it when you only have a single value for any of your fixed effects --
so you may want to expand your prediction data frames, for example for m1
you could predict on those 2 SES values for both sexes (even if you are
only interested in males, you can simply subset the predictions afterwards).

Cheers

Tom


On Fri, 29 May 2020 at 11:01, <r-sig-mixed-models-request using r-project.org>
wrote:
>
>
>
> Message: 1
> Date: Fri, 29 May 2020 17:25:16 +0800
> From: "YA" <xinxi813 using 126.com>
> To: "r-sig-mixed-models" <r-sig-mixed-models using r-project.org>
> Subject: [R-sig-ME] making predictions with MCMCglmm
> Message-ID: <tencent_5701AD3CAB070C10E27910A2F3481E517A06 using qq.com>
> Content-Type: text/plain; charset="utf-8"
>
> Dear list,
>
>
> I am still working on the MCMCglmm predictions. I realized that I didnt
provide a reproducible code in my last email, which makes people here lack
of clues for helping me. I am providing a reproducible example this time
using datasets from the nlme package, so if you have any experience on this
package, please give me some advice on programming the predictions. As you
can see below, same error occured on different models, I guess something is
wrong with my code. Thank you very much.
>
>
> > library(MCMCglmm)
> > library(nlme)
> > data(MathAchieve,package='nlme')
> > data(MathAchSchool,package='nlme')
> > dat=merge(MathAchSchool,MathAchieve,by='School')
> > dat$mathach[dat$MathAch<5]=0
> > dat$mathach[dat$MathAch>=5 & dat$MathAch<15]=1
> > dat$mathach[dat$MathAch>=15]=2
> > dat$mathach=as.factor(dat$mathach)
> > str(dat)
>
>
> # prediction on a continous outcome 'MathAch'
>
> > m1=MCMCglmm(MathAch~Sex+SES,random=~School+SES,dat=dat,verbose=F)
> > summary(m1)
> > predict(m1,data.frame(Sex='Male',SES=c(0.3,-0.8)),type='response')
> Error in FUN(X[[i]], ...) : object 'MathAch' not found
>
>
> # prediction on a binary outcome 'Sex'
> >
m2=MCMCglmm(Sex~SES,random=~School+SES,data=dat,family='categorical',verbose=F)
> > summary(m2)
> >
predict(m2,data.frame(SES=0.5,School='1224'),marginal=NULL,type='response')
> Error in FUN(X[[i]], ...) : object 'Sex' not found
>
>
>
> # prediction on a three category unordered multinomial outcome 'mathach'
>
> >
m3=MCMCglmm(mathach~SES,random=~School+SES,data=dat,rcov=~us(trait):units,family='categorical',verbose=F)
> > summary(m3)
> >
predict(m3,data.frame(SES=0.5,School='1224'),marginal=NULL,type='response')
> Error in FUN(X[[i]], ...) : object 'mathach' not found
>
>
> Best regards,
>
>
> YA
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