On Aug 2, 2018, at 9:24 AM, roee maor <roeemaor at gmail.com> wrote:
Dear list,
I'm using MCMCglmm to run a phylogenetic model where the response is a
3-level ordinal factor (i.e. level 2 is an intermediate phenotype
between 1 and 3), and the predictors include one factorial (foraging
habitat), one ordinal (trophic level), and several continuous
variables.
As far as I know MCMCglmm is the only package that can handle logistic
models for phylogenetically structured multi-level discrete data, but
please correct me if that's not the case.
My problem right now is that I can't get MCMCglmm() to work with the
'family' argument set to "ordinal", although it does work with
"categorical".
Here's the code I'm using:
packageVersion("MCMCglmm")
[1] "R version 3.4.3 (2017-11-30)"
## model specifications:
INphylo <- inverseA(mammaltree, nodes="ALL", scale=TRUE) ## phylogeny with 1399 tips, setting nodes="TIPS" is extremely slow
k <- length(levels(valid$Response))
I <- diag(k-1)
J <- matrix(rep(1, (k-1)^2), c(k-1, k-1))
## categorical model (unordered response) - runs to completion
m1 <- MCMCglmm(Response ~ -1 + trait + ForagingHab + Troph_Lev + Mass + Mean.Diur.Range + Max.Temp.Warmest.M + Temp.Annual.Range + Precip.Driest.Month + PET,
+ random = ~ us(trait):Binomial,
+ rcov = ~ us(trait):units,
+ prior = list(R = list(fix=1, V=(1/k) * (I + J), n = k-1),
+ G = list(G1 = list(V = diag(k-1), n = k-1))),
+ ginverse = list(Binomial=INphylo$Ainv),
+ burnin = 300000,
+ nitt = 3000000,
+ thin = 2000,
+ family = "categorical",
+ data = valid,
+ pl = TRUE)
## ordinal model and error message
m2 <- MCMCglmm(Response ~ -1 + trait + ForagingHab + Troph_Lev + Mass + Mean.Diur.Range + Max.Temp.Warmest.M + Temp.Annual.Range + Precip.Driest.Month + PET,
+ random = ~ us(trait):Binomial,
+ rcov = ~ us(trait):units,
+ prior = list(R = list(fix=1, V=1, n = k-1),
+ G = list(G1 = list(V = diag(k-1), n = k-1))),
+ ginverse = list(Binomial=INphylo$Ainv),
+ burnin = 300000,
+ nitt = 3000000,
+ thin = 2000,
+ family = "ordinal",
+ data = valid,
+ pl = TRUE)
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
contrasts can be applied only to factors with 2 or more levels
## the shape of the data
'data.frame': 1399 obs. of 16 variables:
$ Binomial : chr "Abrocoma_bennettii" "Abrothrix_andinus"
"Abrothrix_jelskii" "Abrothrix_longipilis" ...
$ Response : Factor w/ 3 levels "1","2","3": 1 3 2 2 2 3 2 3 1 3 ...
$ ForagingHab : Factor w/ 7 levels "1","3","4","5",..: 2 2 2 2
2 2 2 2 2 2 ...
$ Troph_Lev : Factor w/ 3 levels "1","2","3": 1 2 2 2 2 3 2 1 1 1 ...
$ Mass : num 250.5 24.9 34.5 38.9 24.5 ...
$ Annual.Mean.Temp : num 12.42 7.26 9.18 9.9 8.62 ...
$ Mean.Diur.Range : num 10.46 13.82 16.16 9.15 7.78 ...
$ Max.Temp.Warmest.M : num 22 16.6 19.1 19.8 17.2 ...
$ Min.Temp.Coldest.M : num 3.77 -3.98 -3.24 2.21 1.46 ...
$ Temp.Annual.Range : num 18.3 20.6 22.4 17.6 15.7 ...
$ Mean.Temp.Warm.Q : num 16 9.2 11.1 13.8 12.2 ...
$ Mean.Temp.Cold.Q : num 8.87 4.49 6.39 5.98 4.87 ...
$ Annual.Precip : num 166 645 558 903 1665 ...
$ Precip.Driest.Month: num 1.74 5.99 6.31 31.31 104.7 ...
$ AET : num 213 482 704 455 361 ...
$ PET : num 1074 1242 1305 677 638 ...
I don't understand what factors the error refers to, because there
sufficient levels in the response even if one is absorbed in the
intercept.
The R-constraint in the prior is specified as suggested in the
MCMCglmm tutorial (fix=1, V=1), but the error message is the same
whether I use this specification or the categorical model
specification (fix=1, V=(1/k)*(I + J)) .
On a side note - what parameters affect the acceptance rates? The
categorical models maintain a rate of around 0.3 so I think the mixing
could be improved.
Any input would be very much appreciated.
Many thanks,
Roi Maor