Thanks David. Turns out the latest version also works if I simulate parental phenotypes too.

Sent from Windows Mail

From: David Duffy<mailto:David.Duffy@qimr.edu.au>
Sent: ıSaturdayı, ı8ı ıFebruaryı ı2014 ı09ı:ı06
To: Wilson, Alastair<mailto:A.Wilson@exeter.ac.uk>
Cc: r-sig-mixed-models@r-project.org<mailto:r-sig-mixed-models@r-project.org>

On Fri, 7 Feb 2014, Wilson, Alastair wrote:

> Thanks - that's v much appreciated. Data file and pedigree structures
> attached.

OK, runs fine with older versions of pedigreemm and lmer

Linear mixed model fit by REML
Formula: size ~ sex + forage + (1 | ID)
    Data: pheno
   AIC  BIC logLik deviance REMLdev
  1160 1180 -574.8     1143    1150
Random effects:
  Groups   Name        Variance Std.Dev.
  ID       (Intercept) 0.35597  0.59663
  Residual             0.73231  0.85575
Number of obs: 400, groups: ID, 400

Fixed effects:
             Estimate Std. Error t value
(Intercept)  5.29569    0.24255  21.833
sex          0.14436    0.09604   1.503
forage       0.88907    0.30644   2.901

Correlation of Fixed Effects:
        (Intr) sex
sex    -0.594
forage -0.717  0.000

The newest version of pedigreemm is failing in pedigreemm::relfactor(),
which is supposed to produce the Cholesky factor of the relationship
matrix, where the error arises in:

solve(t(as(ped,"sparseMatrix")),
       as(factor(labs, levels = ped@label),"sparseMatrix"))

specifically in your exampe, NRM is 500*500 but labs is 400*1 (100
unphenotyped founders)

Cheers, David Duffy.

| David Duffy (MBBS PhD)
| email: David.Duffy@qimrberghofer.edu.au  ph: INT+61+7+3362-0217 fax: -0101
| Genetic Epidemiology, QIMR Berghofer Institute of Medical Research
| 300 Herston Rd, Brisbane, Queensland 4006, Australia  GPG 4D0B994A

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