[R-sig-ME] multivariate mixed nested model

[Claudio]

Dear Thierry,
thanks a lot, it is exactly the kind of suggestion I was looking for!
However, when using (0 + traits|individuals) for the random effect of
individuals I got the message:
"Errore: number of observations (=1431) <= number of random effects
(=1431) for term (0 + traits | individuals); the random-effects
parameters and the residual variance (or scale parameter) are probably
unidentifiable"
The models work when I use for the individual part (1|individuals).

A second further question: in order to include in the model a
continuous fixed covariate, am I doing the right thing when scripting:

mumo2 <- lmer(value~0 + traits + traits:species + traits:covariate (0 +
traits|species:populations) + (1|individuals), mdata, REML=FALSE)

About cbind and MCMCglmm, below there is an example which causes the
message:
"Error in `[<-.data.frame`(`*tmp*`, , response.names, value = c(1, 2,
3,  : missing values are not allowed in subscripted assignments of data
frames"

a = c(1, 2, 3, 5, 5, 7, 8, 9, 4, 5, 7, 8, 9, 4, 1, 2, 3, 5, 5, 4, 5, 7,
8, 9, 4, 1) 
b = c(8, 9, 4, 5, 7, 8, 9, 4, 1, 2, 3, 1, 2, 3, 5, 5, 7, 4, 1, 2, 3, 1,
2, 3, 6, 6)
d = c(8, 9, 4, 5, 5, 7, 8, 9, 4, 5, 7, 8, 9, 4, 1, 2, 3, 2, 3, 1, 2, 3,
5, 5, 7, 6)  
s = c("m", "m", "f", "f", "m", "m", "m", "f", "m", "f", "f", "f", "m",
"f", "f", "f", "m", "m", "m", "m", "m", "f", "m", "f", "f", "f") 
df = data.frame(a, b, d, s)

y<-cbind(a, b, d)
prior <- list(R = list(V = 1, nu = 0.002))
m <- MCMCglmm(y ~ s, family = "gaussian" , data = df, prior = prior,
verbose = FALSE, pl = TRUE)
summary(m)

all the best (and thanks again)
Claudio

Il giorno lun, 30/01/2017 alle 16.02 +0100, Thierry Onkelinx ha
scritto:
> Dear Claudio,
> 
> I this you need to add the interaction with traits to all the fixed
> and random effects. Otherwise you assume that these have the same
> effect for each trait. Note that 0 + traits is identical to traits -
> 1.
> 
> mumo1 <- lmer(value~0 + traits + traits:species + (0 +
> traits|species:populations) + (0 + traits|individuals), mdata,
> REML=FALSE)
> 
> Your second question needs a reproducible example.
> 
> Best regards,
> 
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for
> Nature and Forest 
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality
> Assurance 
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
> 
> 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
> 
> 2017-01-28 16:43 GMT+01:00 Claudio <oppela at gmail.com>:
> > Hi all.
> > I collected six body features (bf1-bf6)from three populations of a
> > salamander and from two populations of another sister species of
> > salamander.
> > I would evaluate how the species (fixed) and population belonging
> > (random) affect the body features, by comparing models built with
> > lme4.
> > For some models, I also want to include bf6 as covariate. Thus, in
> > case
> > of univariate analyses, some models, for example, could be:
> > mo1<-lmer(bf1~species+(1|species:population), data, REML=FALSE)
> > mo2<-lmer(bf1~species+bf6+(1|species:population), data, REML=FALSE)
> > 
> > However, I want to fit multivariate models, and my post is about
> > this.
> > First, I melted the data:
> > mdata<-melt(data, id.vars = c("species", "population", "bf6"),
> > measure.vars = c("bf1", "bf2","bf3","bf4","bf5"), variable.name =
> > "traits)
> > 
> > Now the question.
> > 1) Are the multivariate versions of the models mo1 and mo2 above
> > mumo1<-lmer(value~traits -1 + species + (1|species:populations) +
> > (1|individuals), mdata, REML=FALSE)
> > mumo1<-lmer(value~traits -1 + species + bf6 +
> > (1|species:populations) +
> > (1|individuals), mdata, REML=FALSE)
> > 
> > A secondary question, which in case I will move to a new post:
> > it seemed to me that building multivariate models with MCMCglmm is
> > easier. However, cbind did not work, even without missing values:
> > to
> > your knowledge, is there any issue?
> > 
> > thanks in advance
> > Claudio  
> > 
> > _______________________________________________
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