[R-sig-ME] pairwise combinations of subjects

[Thierry Onkelinx]

Dear Hank,

Here is a solution using the INLA package. This model has two random
effects with identical estimates for each level.

# make sure that sp1 contains each level
old <- x$sp1[n - 1]
x$sp1[n - 1] <- x$sp2[n - 1]
x$sp2[n - 1] <- old

# fit the model
library(INLA)
m <- inla(y ~ c + f(sp1, model = "iid", n = n) + f(sp2, copy = "sp1"), data
= x)
summary(m)
plot(m)

Best regards,


ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
///////////////////////////////////////////////////////////////////////////////////////////

<https://www.inbo.be>


Op ma 15 jul. 2019 om 15:33 schreef Stevens, Hank <hank.stevens using miamioh.edu
>:

> I was hoping someone might point to information or examples of this type of
> problem.
>
> I sometimes encounter data that are derived from interactions between all
> pairwise interactions of subjects (e.g., subject a vs. subject b, subject a
> vs. subject c, subject b vs. subject c). The response is the result of the
> interaction between subjects, and observations are likely to show
> correlations within subject. We are interested in the relation between a
> fixed effect predictor and the response, and not the effects of subject per
> se. For instance,
>
> subj_1   subj_2 . pred  resp
>   a        b       1      5
>   a        c       1.1 .  4
>   b        c       2.5 .  1
>
> where the subj 1 and subj 2 are all the same individuals, but are paired
> with a different partner. It seems as though this might be crossed random
> effects of subj_1 and subj_2. E.g.,
> lmer( resp ~ pred + (1|subj1) + (1|subj2) )
>
> This seems like a design that might be common in breeding....
>
> Many thanks for your thoughts and leads.
>
> Hank Stevens
>
> A more thorough worked example:
>
> library(lme4)
> df <- expand.grid(gl())
> n <- 5
> l <- n*(n-1)/2
> x <- data.frame( matrix(NA, nr=1, nc=2) )
> names(x) <- c("sp1", "sp2")
>
> r <- 1
> for(i in 1:(n-1)){
>   for(j in (i+1):n){
>     x[r,1:2] <- c(i,j)
>     r <- r+1
>   }
> }
>
> set.seed(4)
> x$y <- (x$sp1 + x$sp2) / (n*2) + runif(l)
> set.seed(3)
> x$c <- - (x$sp1 + x$sp2) / (n*2) + runif(l)
>
> ## which design, if any?
> summary( lm(y ~ c, data=x))
> summary( lmer(y ~ c + (1|sp1) + (1|sp2), data=x))
>
> --
> *Dr. Hank Stevens*
> Lab website <http://blogs.miamioh.edu/stevens-lab/>
> PhD Program in Ecology, Evolution, and Environmental Biology
> <http://www.cas.muohio.edu/eeeb/index.html>
> 433 Hughes Hall, Miami University, tel: 513-529-4206
>
>         [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

	[[alternative HTML version deleted]]