Message-ID: <alpine.LMD.2.00.1402081824430.19877@orpheus.qimr.edu.au>
Date: 2014-02-08T09:06:23Z
From: David Duffy
Subject: pedigreemm number of levels per grouping factor
In-Reply-To: <DBBDA54CB159CC41A0A01219B109A4D001BBF2C1@VMEXCHANGEMBS5A.isad.isadroot.ex.ac.uk>
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 at 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 at 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