Hi Marc,
Not sure what's going on here, I can't reproduce your problem. The
default call to predict gives:
> yield.pred <- predict(sp.asr, classify = "Treatment:Entry")
asreml(): 3.0.1 Library: 3.01fe IA32 Run: Tue Feb 09 12:41:02 2010
LogLik S2 DF
-1691.3818 6699.5750 352 12:41:02
-1691.3818 6699.6128 352 12:41:02
-1691.3818 6699.6635 352 12:41:02
-1691.3818 6699.7140 352 12:41:02
Finished on: Tue Feb 09 12:41:02 2010
LogLikelihood Converged
> yield.pred$predictions
$pvals
Treatment Entry predicted.value standard.error est.status
1 A Above 643.0780 26.46003 Estimable
2 A Ankor 611.1636 26.58064 Estimable
so there seems to be no memory problem.
Perhaps we can discuss this further off the list.
Dave.
________________________________
From: ASReml users discussion group [mailto:ASREML-L@DPI.NSW.GOV.AU] On
Behalf Of moragues
Sent: Tuesday, 9 February 2010 1:20 AM
To: ASREML-L@DPI.NSW.GOV.AU
Subject: Re: Predict in ASReml-R leads to 'memory not mapped' crash
Hi Dave,
I am trying to predict a 5 x 24 table. This is the code I am using and
the data is attached:
yield.dat <- read.delim("YGreeley09.asd", h = T, quote = "\"")
yield.dat <- merge(yield.dat, data.frame(Treatment =
levels(yield.dat$Treatment), wplot = factor(1:5))) # Add main plot
factor
yield.dat <- yield.dat[order(yield.dat$Row, yield.dat$Col),]
yield.dat$Row <- factor(yield.dat$Row)
yield.dat$Col <- factor(yield.dat$Col)
sp.asr <- asreml(yield~Treatment, random = ~ Rep +
Entry+Treatment:Entry, data = yield.dat, rcov=~ar1(Row):ar1(Col),
na.method.Y = "include")
yield.pred <- predict(sp.asr, classify = "Treatment:Entry", pworkspace =
16e6)
I also used the "oats" data and example code to figure this out.
Thanks,
Marc.
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Attachments:
Yield data of 24 cultivars grown in 5 water treatments.
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