From: Bruce Southey <southey_at_UIUC.EDU>

Date: Thu, 28 Apr 2005 09:04:14 -0500

Date: Thu, 28 Apr 2005 09:04:14 -0500

Hi,

Since this email got rejected due to the size, I have removed the data.

Regards

Bruce

---- Original message ----

*>Date: Thu, 28 Apr 2005 08:58:20 -0500
*

*>From: Bruce Southey <southey_at_uiuc.edu>
*

*>Subject: Re: Using Poisson data
*

*>To: Rob Brooks <rob.brooks_at_unsw.edu.au>, ASREML-L_at_AGRIC.NSW.GOV.AU
*

*>
*

*>Hi,
*

*>It really helps to examine the data before deciding on the distribution!
*

*>
*

*>The problem is that the data is not Poisson as the mean of the variable
*

'first'

*>is 3397.31 but ranges from zero to 31710! The Poisson parameter should be
*

close

*>to the mean so the distribution would be fairly normal centered around this
*

*>mean value (look at a Poisson distribution with a mean of 3397). However if
*

you

*>look at the frequencies of the variable 'first' you will see that the most
*

*>frequent values occur between zero and about 28 (this is only 1/3 of the data)
*

*>with the rest being rather continuous (occurring once per value).
*

*>
*

*>You really need to look at the data and model before decide what
*

transformation

*>or distribution is appropriate. A gamma/weibull distribution may also be
*

*>appropriate. Natural log and square root are not appropriate transformations
*

*>with the model you fitted. You have some heterogeneity of variance with the
*

*>sires so perhaps the model is missing some important term.
*

*>
*

*>Given the mating structure you probably should be fitting sire, dam and the
*

*>interaction between sire and dam as in the NCII design. Unless you have clones
*

*>or plants, you probably need to address the fact that a sire (dam) can not
*

have

*>two dams (sires) at the same time.
*

*>
*

*>Regards
*

*>Bruce
*

*>
*

*>---- Original message ----
*

*>>Date: Thu, 28 Apr 2005 12:47:22 +1000
*

*>>From: Rob Brooks <rob.brooks_at_unsw.edu.au>
*

*>>Subject: Re: Using Poisson data
*

*>>To: southey_at_UIUC.EDU, ASREML-L_at_AGRIC.NSW.GOV.AU
*

*>>
*

*>>Dear Bruce,
*

*>>
*

*>>Thanks for your response. I would be ecstatic if I could assume normality.
*

*>>I am attaching the .asr file, and, in case you want to see what we have
*

*>>done, the data file. The commands in the .as file are copied below.
*

*>>
*

*>>Many thanks,
*

*>>
*

*>>Rob
*

*>>
*

*>>
*

*>>
*

*>>The job was
*

*>>
*

*>>sire 53 !I
*

*>> dam 9 !I
*

*>> first second third thirdage
*

*>> avg 1st max log1st log2nd log3rd
*

*>>cbd.txt !skip1
*

*>>first !pois !disp ~ mu !r sire sire.dam
*

*>>
*

*>>
*

*>>At 08:43 PM 27/04/2005 -0500, Bruce Southey wrote:
*

*>>>Hi,
*

*>>>There is probably a good chance that you can assume normality. You probably
*

*>>>should at least do so. You may need to first use a square root or natural
*

log

*>>>transformation to stabilize the variance.
*

*>>>
*

*>>>This should provide some basis for using the variances and model checking.
*

*>>>Your
*

*>>>results suggest the the model (including distribution) may not be correct.
*

*>>>
*

*>>>Can you at least provide the relevant part of the .asr file?
*

*>>>
*

*>>>Regards
*

*>>>Bruce
*

*>>>
*

*>>>---- Original message ----
*

*>>> >Date: Thu, 28 Apr 2005 10:41:32 +1000
*

*>>> >From: Rob Brooks <rob.brooks_at_UNSW.EDU.AU>
*

*>>> >Subject: Using Poisson data
*

*>>> >To: ASREML-L_at_AGRIC.NSW.GOV.AU
*

*>>> >
*

*>>> >Dear ASREMLers
*

*>>> >
*

*>>> >I have recently used !POIS to estimate variances of poisson distributed
*

*>>> >data from a half-sib design. The dire and dam within sire components are
*

*>>> >significantly greater than zero but appear impossibly small relative to
*

the

*>>> >within groups variance. I gather this is an issue to do with the scaling
*

of

*>>> >poisson variances, and it seems from searching back through the ASREML
*

*>>> >discussions that there is no appropriate way to use these variance
*

*>>> >estimates to calculate heritabilities.
*

*>>> >
*

*>>> >Likewise, I gather that there is no way to estimate covariances where one
*

*>>> >or more traits follows a poisson distribution.
*

*>>> >
*

*>>> >Am I right about these points or am I missing something?
*

*>>> >
*

*>>> >Further, would it be appropriate to use the sire breeding values for each
*

*>>> >trait given in the .sln file to estimate the correlation among breeding
*

*>>> >values as a rough estimate of the genetic correlation?
*

*>>> >
*

*>>> >Your thoughts on these issues would be much appreciated.
*

*>>> >
*

*>>> >Rob
*

*>>> >
*

*>>> >
*

*>>>
*

*>>............................................................................
*

*>>> >............................
*

*>>> >School of Biological, Earth and Environmental Sciences
*

*>>> >The University of New South Wales
*

*>>> >Kensington, Sydney 2052
*

*>>> >NSW, Australia
*

*>>> >
*

*>>> >http://www.bees.unsw.edu.au/research/groups/brookslab/brookslab.html
*

*>>________________
*

*>>________________
*

*>> ASREML [25 May 1999] analysing herit of Felix's callbox data
*

*>> 28 Apr 2005 12:44:53.921 8.00 Mbyte h2
*

*>> QUALIFIERS: !skip1
*

*>> Reading cbd.txt FREE FORMAT skipping 1 lines
*

*>> Univariate analysis of first
*

*>> Using 785 records [of 785 read from 785 lines of
*

*>cbd.txt ]
*

*>> Model term Size Type COL Minimum Mean Maximum #zero
*

*>#miss
*

*>> 1 sire 53 Factor 1 1 25.8510 52 0
*

*>0
*

*>> 2 dam 9 Factor 2 1 4.1732 9 0
*

*>0
*

*>> 3 first 1 Variate 3 1.000 3397. 0.3171E+05 115
*

*>0
*

*>> 4 second 1 Covariat 4 1.000 0.1287E+05 0.4463E+05 85
*

*>119
*

*>> 5 third 1 Covariat 5 1.000 0.1356E+05 0.4180E+05 43
*

*>553
*

*>> 6 thirdage 1 Covariat 6 1.000 1.025 2.000 0
*

*>0
*

*>> 7 avg 1 Covariat 7 0.5000 7790. 0.3068E+05 41
*

*>0
*

*>> 8 max 1 Covariat 8 1.000 1.167 3.000 0
*

*>0
*

*>> 9 log1st 1 Covariat 9 0.6931 7.697 10.71 41
*

*>0
*

*>> 10 log2nd 1 Covariat 10 0.6931 4.803 10.36 115
*

*>0
*

*>> 11 log3rd 1 Covariat 11 0.6931 7.279 10.71 85
*

*>119
*

*>> 12 mu 1 Constant Term
*

*>> 13 sire.dam 477 Interaction 1 sire : 53 2 dam :
*

*>9
*

*>> Forming 532 equations: 2 dense
*

*>> Initial updates will be shrunk by factor 0.010
*

*>> Data distribution and link: Poisson; Log Mu=exp(XB)
*

*>> Deviance adjust= -6453748.50
*

*>> Deviance adjust= -20843224.0
*

*>> NOTICE: 175 (more) singularities,
*

*>> LogL=-.104197E+08 S2= 1880.8 784 df 0.10000 0.10000 1.0000
*

*>>
*

*>> Deviance adjust= -7808407.50
*

*>> Deviance adjust= -3843321.25
*

*>> LogL=-.191972E+07 S2= 2799.7 784 df 0.10000E-010.10000E-01 1.0000
*

*>>
*

*>> Deviance adjust= -2797413.50
*

*>> Deviance adjust= -2824858.50
*

*>> LogL=-.141017E+07 S2= 3326.1 784 df 0.10000E-020.10000E-02 1.0000
*

*>>
*

*>> Deviance adjust= -2719273.75
*

*>> Deviance adjust= -3044960.25
*

*>> LogL=-.152009E+07 S2= 4039.8 784 df 0.10000E-030.32401E-03 1.0000
*

*>>
*

*>> Deviance adjust= -2984056.75
*

*>> Deviance adjust= -3299057.25
*

*>> LogL=-.164711E+07 S2= 4678.4 784 df 0.77055E-040.15373E-03 1.0000
*

*>>
*

*>> Deviance adjust= -3281172.75
*

*>> Deviance adjust= -3356524.50
*

*>> LogL=-.167585E+07 S2= 4862.3 784 df 0.63442E-040.13519E-03 1.0000
*

*>>
*

*>> Deviance adjust= -3355855.50
*

*>> Deviance adjust= -3374219.75
*

*>> LogL=-.168469E+07 S2= 4898.1 784 df 0.61806E-040.13088E-03 1.0000
*

*>>
*

*>> Deviance adjust= -3374192.00
*

*>> Deviance adjust= -3377668.25
*

*>> LogL=-.168642E+07 S2= 4904.6 784 df 0.61478E-040.13009E-03 1.0000
*

*>>
*

*>> Deviance adjust= -3377667.25
*

*>> Deviance adjust= -3378367.75
*

*>> LogL=-.168677E+07 S2= 4905.9 784 df 0.61414E-040.12993E-03 1.0000
*

*>>
*

*>> Deviance adjust= -3378367.75
*

*>> Deviance adjust= -3378509.25
*

*>> LogL=-.168684E+07 S2= 4906.1 784 df 0.61401E-040.12990E-03 1.0000
*

*>>
*

*>> Final parameter values 0.61398E-040.12990E-03 1.0000
*

*>>
*

*>> Variance heterogenity factor [Deviance/DF] 4309.32
*

*>>
*

*>> Source Model terms Gamma Component Compnt/StndErr
*

*>%C
*

*>> sire 53 52 0.613984E-04 0.301228 2.64 0 P
*

*>> sire.dam 477 303 0.129896E-03 0.637287 5.41 0 P
*

*>> Variance 785 784 1.00000 4906.12 16.28 0 U
*

*>>
*

*>> Analysis of Variance DF F-incr F-adj StndErrDiff
*

*>> 12 mu 1 5648.08 5648.08
*

*>>
*

*>> Solution Standard Error T-value T-prev
*

*>> 12 mu
*

*>> 1 7.83539 0.104258 75.15
*

*>> 1 sire 52 effects fitted
*

*>> 13 sire.dam 303 effects fitted
*

*>> Finished: 28 Apr 2005 12:44:55.875 LogL Converged
*

*>>________________
*

*>>............................................................................
*

*>>............................
*

*>>School of Biological, Earth and Environmental Sciences
*

*>>The University of New South Wales
*

*>>Kensington, Sydney 2052
*

*>>NSW, Australia
*

*>>
*

*>>http://www.bees.unsw.edu.au/research/groups/brookslab/brookslab.html
*

*>
*

*>
*

Received on Wed Apr 28 2005 - 09:04:14 EST

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