Re: SMALL TROUBLEM

A few typos; I have changed with a # comment

On Thu, 2009-07-23 at 03:56 -0700, Sergio I. Roman Ponce wrote:
> Dar Arthur.
>
> I am runnind ASREML 2.0 with a animal model.
>
> The program is
> ANIM !P ####
> SIRE
> DAM
> HTD !A 52424 # Need to spcify how many you expect when >1000 all up
> YYC !A
> MMC !A
> BREED !A
> NLACT !A
> AGECALF
> DIM !A
> Y
> DATA.dat
> PED.PED
> !MVREMOVE
> !MAXIT1000
> Y ~ mu MMC NLACT BREED DIM AGECALF NLACT ,
>
> !r Anim ide(anim) 0.12 !F HTD # three changes
> 0 0 0
>
> Then the message is
>
> INCREASE DECLARED LEVELS FOR FACTOR HTD.
>
> THe levels of HTD are 52424...
>
> What can i do?
>
>
> ---
> Mc Sergio I. Román Ponce
> Investigador Titular Campo Experimental “Valles Centrales de Oaxaca”
> - CIRPAS
> Teléfonos:
> + 52 951 521 55 02
>
> + 52 951 204 50 30 Celular
> Correos Electrónicos:
> roman.sergio_at_inifap.gob.mx
>
> www.inifap.gob.mx
>
>
> --- On Thu, 7/23/09, Arthur <asremlforum_at_VSNI.CO.UK> wrote:
>
> From: Arthur <asremlforum_at_VSNI.CO.UK>
> Subject: Re: heritability estimation for binary and
> quantitative traits
> To: ASREML-L_at_DPI.NSW.GOV.AU
> Date: Thursday, July 23, 2009, 1:32 PM
>
> Dear Mahood,
>
> In this regard, I have attached two data files,
> one for quantitative trait and another is for qualitative
> trait.
> and thereby I have attached my result files also.
> Frankly speaking, I am really confused about my next steps:
> 1. Firstly, I will really be very glad if I am assured that
> my *.as and *.pin files are right on track. i.e. for
> heritability
> estimation my codes are right in terms of the variance
> components
> I got in *.asr file and are specified in the *.pin file.
>
> * For body weight, the analysis is fine.
> Heritability looks reasonable; coding is fine.
> You do not need the * or !M0 on the lines
> id !P
> sire !P dam !P
>
>
> 2. Secondly, I have used those factors and their interactions
> in the model for quantitative trait (body weight) which were
> shown as highly significant ( sex [p<0.0001], birth_year
> [p<0.0001],
> test_age[p<0.0001], sex*test_age [p<0.01] using SAS proc glm
> procedure.
> - In this case if I use all this factors I get 7 singularities
> in the design matrix.
> - In contrast, if I exclude the interaction (sex*test_age)
> which is also significant,
> then, I get only 3 singularities cases. So, I am really
> confused that which model I should choose.
>
>
> The singularities are not a problem. It is just that the model
> is over parameterised.
> Analysis of Variance NumDF DenDF F_inc Prob
> 8 mu 1 108.0 24689.41 <.001
>
> 4 sex 1 849.7 99.17 <.001
> 5 birth_year 4 791.2 23.35 <.001
> 6 test_age 2 853.9 14.03 <.001
> 9 sex.test_age 2 836.1 3.74 0.026
>
> Sex has 2 levels but 1 DF,
> birthyear has 5 levels but 4 DF
> testage has 3 levels but 2 DF
> sex.testage has 6 levels but 2 DF
>
> Now you report different significance levels from SAS,
> but I do not think it is the same model [here you have the
> animal model]
> in any case, the first 3 are equivaalent.
> I'm not surprised sex.test_age is slightly different under the
> animal model.
>
> I expect you will get the same answers from ASReml as from SAS
> if you fit the same model.
>
> Note there is also an !FCON which will give additional
> complementary F tests.
>
>
> [ Actually, how much this differences in singularities may
> play
> a role in the model output specially when I am getting the
> convergence
> in every case. I mean, how can I deal the situation like this
> for
> inclusion or exclusion of factors /interactions?
> In that case the factor was significant
>
> I would leave sex.test in the fitted model though it is now
> significant at 5% not 1%.
>
> The coding of the binary analysis looks fine too.
> It is harder to formally test fixed effects but I would
> suggest that only the test_age regression is
> significant.
>
>
> Using an animal model for binary data generally works in the
> sense that you get an answer.
> Simulation studues though tend to suggest the answer is not
> what you might predict.
> I'e it tends to underestimate heritabiliy in simulation
> studies but is very dependent on
> da. If you give a suggestion on my query that will be really
> appreciable]
>
> 3. Thirdly, for the binary analysis , I used !BIN !LOGIT. In
> this regard, I would like to request you to take a glance on
> my *.asr, *.pin file for the correctness.
> - I used a conversion factor of 3.29 in the pin file for the
> variance part I got in *.asr file.
> -Do you think that this has been done correctly plotted for
> the right variance ***component ? Moreover, can I draw any
> valid conclusion on heritability estimates, for a qualitative
> trait based on binary analysis, specially from my analysis
> codes?
>
> For
>
>
>
> ______________________________________________________________
>
> Arthur Gilmour
>
> Retired Principal Research Scientist (Biometrics)
>
>
>
>
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>
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>
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-- 
Best Wishes,
Arthur Gilmour
Adjunct Professor
School of Computing and Mathematics
Charles Sturt University
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