Re: Is my model right for survival rate?

Hi, I use the sire-dam model , the heritability is 0.07±0.05. I found
that sire-dam model was used from those relative papers about estimating
genetic paramters of survival by challenge test. My parameter file is:

2007 survival rate analysis of challenge test of wssv by animal ,sire or
dam model
animal !P
sire !P
dam !P
fullsib 45 !I
tank 3 !I
survival

wssv2007.ped !MAKE
wssv20071h.dat !SKIP 1 !MAXIT 200

#sire-dam model
survival !bin !probit ~ mu tank !r sire dam fullsib 0.25

is it right? thanks!

arthur.gilmour_at_DPI.NSW.GOV.AU 写道:
> Dear luan sheng
>
> re:
> I am the beginner of asreml. I am estimating the variance of survival
> rate which is from the challenge test data of turbot. Turbot is one of
> marine fishies.
> this is my model:
> survival !bin !probit !wt=0 ~ mu !r animal fullsib 0.2
> Is the model right for this analysis? which is right for survival rate
> logit or probit link function?
> The format of data file is just like this:
> animal Sire Dam fullsib survival (binary,0 for death, 1 for survival)
> 20070028 2006041 20060159 3 0
> 20070029 2006041 20060159 3 0
> 20070030 2006041 20060159 3 0
> 20070031 2006041 20060159 3 0
>
> I strongly suggest you also fit a sire model, and compare the results,
> as the animal model can give biassed estimates of the variance with
> binomial data.
> In this case, you appear to have large families so it might be fine.
>
> survival !bin !probit !wt=0 ~ mu !r Sire fullsib 0.2
>
> I cannot tell from the data whether Sire and Dam provide the same grouping
> of records
> (i.e. you only have full sib families). If you have half sib families,
> then also\
> fit Dam.
>
>
> As a new user, you should try several forms of the models to get a feel
> for how models compare. e.g. You can also fit the models as if the
> traits were
> normal. Your model is simple, and appears to have an incidence around 50%
> so a Normal analysis should be consistent with the binary analysis.
>
> For binary data, you can omit the '!wt=0' qualifier as 0 is the default
> setting.
>
>
>
>
> May Jesus Christ be gracious to you,
>
> Arthur Gilmour, His servant .
>
> Mixed model regression mapping for QTL detection in experimental crosses.
> Computational Statistics and Data Analysis 51:3749-3764 now available at
> http://dx.doi.org/10.1016/j.csda.2006.12.031
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> Principal Research Scientist (Biometrics)
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--
   Yours sincerely,
   luan sheng
   ----------------------------------------
   Yellow Sea Fisheries Research Institute
   Chinese Academy of Fisheries Sciences
   Nanjing Road 106
   Qingdao 266071
   China
   luansheng_at_gmail.com
   luansheng_at_ysfri.ac.cn
   ----------------------------------------