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*To*: asreml@chiswick.anprod.csiro.au*Subject*: Schall method and ASREML*From*: Arthur Gilmour <gilmoua@ornsun.agric.nsw.gov.au>*Date*: Thu, 26 Aug 1999 16:11:52 +1000 (EST)*Reply-To*: Arthur Gilmour <gilmoua@ornsun.agric.nsw.gov.au>*Sender*: asreml-owner@ram.chiswick.anprod.csiro.au

Dear friends, As you are probably all aware, ASREML uses the Schall method for GLMMs In his paper, Schall supplies some cell irradiation data. I enclose that data and an ASREML job which runs several models on it. The 27 units represent 9 occasions, 3 dishes per occasion and 400 cells per dish. Schall fits 3 models which in ASREML notation are surv ~ mu surv ~ mu !r Occ surv ~ mu !r Occ units ASREML gives the same variance components as Schall gives for Occ and units in these models. Schall also reports a residual variance which I cannot reproduce, even in the simplest case. The residuals returned by ASREML are (O-E)/d where O is the observation, E is the fitted expected value and d is the derivative (if E=np, d=np(1-p) ) and the value beside the residual in the .sln file is (in this case) logit(p) So it is quite simple to calculate residuals on the observed scale. If anyone is interested and has worked out exactly what Schall is doing, please let me know. Arthur <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Arthur Gilmour PhD mailto:Arthur.Gilmour@agric.nsw.gov.au Senior Research Scientist (Biometrics) fax: <61> 2 6391 3899 NSW Agriculture <61> 2 6391 3922 Orange Agricultural Institute telephone work: <61> 2 6391 3815 Forest Rd, ORANGE, 2800, AUSTRALIA home: <61> 2 6362 0046 ASREML is still free by anonymous ftp from pub/aar on ftp.res.bbsrc.ac.uk or point your web browser at ftp://ftp.res.bbsrc.ac.uk/pub/aar/ To join the asreml discussion list, send the message subscribe mailto:asreml-request@chiswick.anprod.CSIRO.au To send messages to the list, mailto:asreml@chiswick.anprod.CSIRO.au Asreml list archive: http://www.chiswick.anprod.csiro.au/lists/asreml <> <> <> <> <> <> <> "Christ Jesus came into the world to save sinners" I Timothy 1:15. <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

Schall cell irradiation data occasion 9 survived !/V3 total schall.dat !DOPART $1 !PART 1 ! surv !bin !total total ~ mu !r occ ! disp cal from residuals is 32.84/26 = 1.26 [Schall gives 1.810] surv !bin !total total ~ mu !r occ !PART 2 ! surv !bin !total total !disp ~ mu !r occ ! dispersion calc as 1.819 surv !bin !total total !disp ~ mu !r occ !PART 3 ! surv !bin !total total ~ mu !r occ units ! dispersion (see below) is 9.3882 / 26 = .36 [Schall gives 0.937] surv !bin !total total ~ mu !r occ units !PART 4 ! surv !bin !total total !disp ~ mu !r occ units ! fitted this way is overparameterised and disp does to zero surv !bin !total total !disp ~ mu !r occ units !PART 5 ! surv !bin !total total ~ mu ! gives dispersion calculated from residuals of 18.96 (see below) ! Schall gives 18.09 surv !bin !total total ~ mu !PART 6 ! surv !bin !total total !disp ~ mu ! gives dispersion of 18.96 = 493/26 surv !bin !total total !disp ~ mu !PART 0 pchi_function(file='schall.sln'){ # ASREML residuals are (O-E)/npq; predicted value is logit(p) # Want (O-E)^2/npq resdf_read.table(file) res_resdf$V3[resdf$V1=='Residual'] upv_resdf$V4[resdf$V1=='Residual'] pv _ exp(upv)/(1+exp(upv)) pchi_400*sum(pv*res^2*(1-pv)) } S> pchi('schall5.sln') #Schall gets 470.34 [22.6 less] [1] 492.97 S> pchi('schall3.sln') #Schall gets 24.36 [15 more] [1] 9.3882 S> pchi('schall2.sln') [1] 33.174 S> pchi('schall1.sln') #Scall gets 47.06 [14.2 more] [1] 32.836 Models 5 1 and 3 are the one Schall fits and ASREML agrees with respect to the extra variance components.

1 178 400 1 193 400 1 217 400 2 109 400 2 112 400 2 115 400 3 066 400 3 075 400 3 080 400 4 118 400 4 125 400 4 137 400 5 123 400 5 146 400 5 170 400 6 115 400 6 130 400 6 133 400 7 200 400 7 189 400 7 173 400 8 088 400 8 076 400 8 090 400 9 121 400 9 124 400 9 136 400

**Follow-Ups**:**Re: Schall method and ASREML***From:*averbyla@biometricssa.adelaide.edu.au

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