From: Nilaya Sharma <nilaya.sharma_at_gmail.com>

Date: Fri, 15 Apr 2011 22:29:02 -0400

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Sat 16 Apr 2011 - 05:46:13 GMT

Date: Fri, 15 Apr 2011 22:29:02 -0400

My question was how can we estimate effects and define correct model equivalent to SAS code provided.

On Fri, Apr 15, 2011 at 10:21 PM, Nilaya Sharma <nilaya.sharma_at_gmail.com>wrote:

> Hi R community,

*>
**> I am new bird to R and moved recently from SAS. I am no means expert on
**> either but very curious learner. So your help crucial for me to learn R.
**> I have already got positive expression.
**>
**> I was trying to fit a mixed model in animal experiment but stuck at simple
**> point. The following similar example is from SAS mixed model pp 212.
**>
**> # data
**>
**> genetic_evaluation <- read.table(textConnection("
**> sire dam adg
**> 1 1 2.24
**> 1 1 1.85
**> 1 2 2.05
**> 1 2 2.41
**> 2 1 1.99
**> 2 1 1.93
**> 2 2 2.72
**> 2 2 2.32
**> 3 1 2.33
**> 3 1 2.68
**> 3 2 2.69
**> 3 2 2.71
**> 4 1 2.42
**> 4 1 2.01
**> 4 2 1.86
**> 4 2 1.79
**> 5 1 2.82
**> 5 1 2.64
**> 5 2 2.58
**> 5 2 2.56"), header = TRUE)
**>
**> # my R practice codes
**> require (lme4)
**> lmer(adg ~ 1 + (1|sire) + (1|dam/sire), data=genetic_evaluation)
**>
**> ****error message********************************************88
**> Error: length(f1) == length(f2) is not TRUE
**> In addition: Warning messages:
**> 1: In sire:dam : numerical expression has 20 elements: only the first used
**> 2: In sire:dam : numerical expression has 20 elements: only the first used
**>
**> **********************how can I estimate the BLUP effects?*************
**> #equavalent code in SAS
**> proc mixed data=genetic_evaluation;
**> class sire dam;
**> model adg= / ddfm=kr;
**> random sire dam(sire);
**> estimate 'sire 1 BLUP "broad" '
**> intercept 1 | sire 1 0;
**> estimate 'sire 1 BLUP "narrow" '
**> intercept 2 | sire 2 0
**> dam(sire) 1 1 0 0 0 0 0 0 0 0 / divisor=2;
**> estimate 'sire 1 BLUP with dam 1'
**> intercept 1 | sire 1 0
**> dam(sire) 1 0;
**> ods select CovParms Estimates;
**> run;
**>
**> # Estimate statement define predictable functions. All fixed effect
**> cofficient must appear first and then random effect coefficients. The fixed
**> and random
**> #effect cofficient are seperated by |
**>
**> ****************Expected outputs according to SAS
**> *************************************
**> Estimate
**> sire 1 BLUP "broad 2.2037
**> sire 1 BLUP "narrow" 2.1609
**> sire 1 BLUP with dam1 2.1002
**>
**> Data details:
**> The data is animal science data in which five sires were randomly sampled
**> from the population and were randomly mated with two dams.
**> Two offspring per sire dam combination were measured. Average daily gain
**> was recorded. We are interested in breeding value of ith sire(means that
**> which
**> gives offsprings with higher gain
**>
**> NIL
**>
*

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