Have you tried re-scaling your predictors? (Your 1st warning says,
"re-scale variables".)

This can be done with the arm package:

install.packages("arm"); library(arm)
model.standardized = standardize(model,standardize.y=FALSE);
model.standardized

Christian


On Sun, Jun 15, 2014 at 2:57 PM, Sofía Brandariz <brandarizsofia@gmail.com>
wrote:

> Dear Mr/Mrs,
>
> I am writing to you because I have convergence problems with my analysis
> using lmer function. I am trying to implement a multi-trait GWAS analysis
> for physiological traits in wheat, with 18337 SNPs, 384 genotypes and 20
> traits. When I use Naive model (no correction structure) I have no
> problems. When I incorporate the first two PC in the model, I obtain 50
> warnings like:
>
> 44: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
>  ... :
>   Model is nearly unidentifiable: large eigenvalue ratio
>  - Rescale variables?
> 45: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
>  ... :
>   Model failed to converge: degenerate  Hessian with 1 negative eigenvalues
> 46: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
>  ... :
>    Hessian is numerically singular: parameters are not uniquely determined
> 47: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
>  ... :
>   Model failed to converge: degenerate  Hessian with 2 negative eigenvalues
> 48: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
>  ... :
>   Model failed to converge: degenerate  Hessian with 2 negative eigenvalues
> 49: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
>  ... :
>   Model failed to converge: degenerate  Hessian with 1 negative eigenvalues
> 50: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
>  ... :
>   Model failed to converge: degenerate  Hessian with 2 negative eigenvalues
>
> I have tried with different optimizers like you suggested to other
> investigators but all of them gave me warnings like the one I showed you.
>
> My model is
>
>     out2=NULL
>     for(i in 4:ncol(MT.data)){
>       Marker=MT.data[,i]
>       modelo=lmer(MEAN~TRAIT + Marker:TRAIT + (1|GEN) + (1|PC1) + (1|PC2),
> data=MT.data2)
>
>       out<-cbind(anova(modelo)[2,], as.character(rownames(b)[i-3]),
> (t(b[i-3,1:2])))
>       out2<-rbind(out2, out)
>     }
>
> MEAN: vector with BLUEs for each trait
> TRAIT: vector that indicates the trait for each row
> Marker: SNPs column, each SNPs is repeated the number of traits
> GEN: vector that indicates id of each genotype, each is repeated the number
> of traits
> PC1 and PC2: are principal components, each is repeated the number of
> traits
>
> What should I do? Thank you in advanced
> Sincerely,
> Sofía Brandariz
>
>         [[alternative HTML version deleted]]
>
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>


-- 
Christian Perry
Ph.D. Candidate
New York Cooperative Fish and Wildlife Research Unit
Department of Natural Resources
Cornell University
618 Bradfield Hall
Ithaca, NY 14853
Phone: 607-255-2839
email: pcp35@cornell.edu

*"How often does celebrity thus taunt her subjects with fickle hints of
everlasting glory!  That spring of 1845, Frémont must have sensed that the
world lay before him.  He was only thirty-two years old, and all his
grandest campaigns lay still inside his head."  from *A Newer World: Kit
Carson, John C. Frémont, and the Claiming of the American West*, 2000, by
David Roberts, pg. 138.*

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