pedigreemm number of levels per grouping factor

I???m trying to fit a pedigree-based mixed model (ie animal model) to
estimate a genetic variance and a couple of fixed effects in a
simulated data set. I???ve fitted the model using AsReml-R and
MCMCglmm. I wanted to try using pedigreemm but get a error

Error in checkNlevels(reTrms$flist, n = n, control) : number of
levels of each grouping factor must be < number of observations

I *think* this is telling me I don???t have multiple observations per
factor level (here ID) which is true. However, I shouldn???t need
repeat measures to estimate the random effect variance here (i.e.
VA), so am assuming I have not got the code (below) right. Any
pointers v gratefully received.

Alastair

I'm not sure, but I guess that this is one of the cases where
individual-level random effects *do* make sense in a LMM context.  I
don't know whether pedigreemm allows you to pass arguments through to
lmer, but if it does you can use
control=lmerControl(check.nobs.vs.nlev="ignore") to suppress this check.
 You might also be able to do this globally via

options(lmerControl=list(check.nobs.vs.nlev="ignore"))

  (The maintainer of the package might want to consider suppressing this
check by default, if this is a common issue ...)

  Ben Bolker

############### library(pedigreemm) setwd("c:/prussia/")

voles<-read.table("voles.txt", header=T) 
voles$ID<-as.factor(voles$ID) voles$sex<-as.factor(voles$sex)

volesPED<-read.table("volesPED.txt",header=T) 
volesPED2<-pedigree(volesPED$SIRE,volesPED$DAM,volesPED$ID)

model3<-pedigreemm(size~forage+sex+(1|ID),
pedigree=list(ID=volesPED2), data=voles, REML=TRUE)

It would be nice if the example were reproducible ...

Thanks for the suggestion Ben. Sadly it didn't provide a solution. The error message indicates the variance component I am after is unidentifiable, which would not be the case if I have correctly got the random ID effect linked to the pedigree.

Error in .sortCsparse(.Call(dtCMatrix_sparse_solve, a, b)) : 
  Dimensions of system to be solved are inconsistent
In addition: Warning message:
In checkZrank(reTrms$Zt, n = n, control, nonSmall = 1e+06) :
  number of observations <= rank(Z); variance-covariance matrix will be unidentifiable

I'll maybe contact the package maintainer directly unless anyone else has thoughts/experience with pedigreemm.

Alastair


-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Ben Bolker
Sent: 05 February 2014 18:35
To: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] pedigreemm number of levels per grouping factor

I???m trying to fit a pedigree-based mixed model (ie animal model) to 
estimate a genetic variance and a couple of fixed effects in a 
simulated data set. I???ve fitted the model using AsReml-R and 
MCMCglmm. I wanted to try using pedigreemm but get a error

Error in checkNlevels(reTrms$flist, n = n, control) : number of levels 
of each grouping factor must be < number of observations

I *think* this is telling me I don???t have multiple observations per 
factor level (here ID) which is true. However, I shouldn???t need 
repeat measures to estimate the random effect variance here (i.e.
VA), so am assuming I have not got the code (below) right. Any 
pointers v gratefully received.

Alastair

I'm not sure, but I guess that this is one of the cases where individual-level random effects *do* make sense in a LMM context.  I don't know whether pedigreemm allows you to pass arguments through to lmer, but if it does you can use
control=lmerControl(check.nobs.vs.nlev="ignore") to suppress this check.
 You might also be able to do this globally via

options(lmerControl=list(check.nobs.vs.nlev="ignore"))

  (The maintainer of the package might want to consider suppressing this check by default, if this is a common issue ...)

  Ben Bolker

############### library(pedigreemm) setwd("c:/prussia/")

voles<-read.table("voles.txt", header=T)
voles$ID<-as.factor(voles$ID) voles$sex<-as.factor(voles$sex)

volesPED<-read.table("volesPED.txt",header=T)
volesPED2<-pedigree(volesPED$SIRE,volesPED$DAM,volesPED$ID)

model3<-pedigreemm(size~forage+sex+(1|ID),
pedigree=list(ID=volesPED2), data=voles, REML=TRUE)

It would be nice if the example were reproducible ...

_______________________________________________
R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models