Re: [R] lme4 problem: model defining and effect estimation ------ question from new bird to R community from SAS community

From: Nilaya Sharma <nilaya.sharma_at_gmail.com>
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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