R help - Oct 2012 - Error in lmer: asMethod(object) : matrix is not symmetric [1, 2]

Dear R Users,

I am having trouble with lmer. I am looking at recombinant versus non 
recombinant individuals. In the response variable recombinant 
individuals are coded as 1's and non-recombinant as 0's. I built a model
with 2 fixed factors and 1 random effect. Sex (males/females) is the 
first fixed effect and sexual genotype (XY, YY, WX and WY) the second 
one. Sexual Genotype is nested in sex. XY and YY individuals are males 
and WX and WY females. I crossed 8 XY males with 8 WY females and 8 YY 
males with 8 WX females. Each cross corresponds to a family (i.e. family 
1 to 8 for XY x WY and family 9 to 16 for YY WX). For each family I have 
20 offspring. Family is nested in sexual genotype as a random factor.

My data looks like:

reps<-factor(sample(c(0,1),640,replace=T,prob=c(0.95,0.05)))

sex<-factor(rep(c("M","F"),each=320))

geno<-factor(rep(c("XY","YY","WY","WX"),each=160))

fam<-factor(rep(c(1:8,9:16,1:8,9:16),each=20))

dat<-data.frame(reps,sex,geno,fam)

and I built the following model:

lmer(reps~sex+geno+(1|fam),data=dat,family=binomial)

and tried also

lmer(reps~sex/geno+(1|fam),data=dat,family=binomial)

but I keep getting this error:

asMethod(object) : matrix is not symmetric [1,2]

Does someone have an idea where the error might come from?

Thank you very much in advance!

Olivier

Sex-chromosome turnovers induced by deleterious mutation load

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Olivier Blaser <olivier.blaser <at> unil.ch> writes:

> I am having trouble with lmer. I am looking at recombinant versus non 
> recombinant individuals. In the response variable recombinant 
> individuals are coded as 1's and non-recombinant as 0's. I built a
model
> with 2 fixed factors and 1 random effect. Sex (males/females) is the 
> first fixed effect and sexual genotype (XY, YY, WX and WY) the second 
> one. Sexual Genotype is nested in sex. XY and YY individuals are males 
> and WX and WY females. I crossed 8 XY males with 8 WY females and 8 YY 
> males with 8 WX females. Each cross corresponds to a family (i.e. family 
> 1 to 8 for XY x WY and family 9 to 16 for YY WX). For each family I have 
> 20 offspring. Family is nested in sexual genotype as a random factor.
> 
> My data looks like:
>
reps<-factor(sample(c(0,1),640,replace=T,prob=c(0.95,0.05)))
sex<-factor(rep(c("M","F"),each=320))
geno<-factor(rep(c("XY","YY","WY","WX"),each=160))
fam<-factor(rep(c(1:8,9:16,1:8,9:16),each=20))
dat<-data.frame(reps,sex,geno,fam)

with(dat,table(sex,geno))
with(dat,table(sex,geno,fam))

 Follow-ups to this question should go to r-sig-mixed-models

 The slightly obscure error message is caused by a glitch
in lme4's printing code, where it tries to print a model
that hasn't been successfully fitted in the first place.
I actually get a **slightly** more informative error message
(maybe with a slightly later version of the package):

g1 <- glmer(reps~sex+geno+(1|fam),data=dat,family=binomial)
Error in mer_finalize(ans) : Downdated X'X is not positive definite, 1.

[although lmer() automatically calls glmer() for GLMMs, I
 prefer to use glmer() explicitly]

  To diagnose/show what the problem is, let's try fitting in glm.
It works, but the answer indicates that
your design matrix is rank-deficient/overfitted:
    
(g0 <- glm(reps~sex+geno,data=dat,family=binomial))

i.e., the 'genoYY' estimate is NA, because once 'sexM',
'genoWY', and 'genoXY' parameters are fitted the model is
completely determined.

  Unlike [g]lm, [g]lmer can't handle rank-deficient/overfitted
fixed-effect design matrices (as I understand it, because the
somewhat fancier/more efficient linear algebra primitives used
in lme4 don't handle this case).  Therefore, you have to figure
out a way to code your design matrix that will work.  At the moment
I don't know of an elegant way to do that, but here's a hack:

## extract model matrix and drop intercept and genoYY dummy variables
mm <- model.matrix(g0)[,2:4]  
dat2 <-data.frame(reps,mm,fam)
g2 <- glmer(reps~sexM+genoWY+genoXY+(1|fam),data=dat2,family=binomial)

  I don't know if this gives you the answer you want or not ...