[R] Duncan's MRT: limitations to qtukey() function?
Peter Dalgaard
P.Dalgaard at biostat.ku.dk
Tue Jan 22 17:16:52 CET 2008
Andrea Onofri wrote:
> Dear all,
>
> I'm using R to perform multiple comparison testing on agriculture
> genotype trials. To perform the Duncan's MRT, I use the qtukey()
> function with the following syntax:
>
> qtukey(p = ((1 - 0.05) ^ (pos - 1)), nmeans = pos, df = ni)
>
> I experience a strange behaviour when the number of means in the trial
> and the number of residual degrees of freedom (ni) becomes high
> (orientatively pos > 30 and ni > 60), i.e. the function returns NaN
> together with the following message:
>
> convergence failed in 'qtukey'
>
> Running the code below should reproduce a statistical table for
> Duncan's MRT, such those reported on old books of agricultural
> statistics (ex. Steel and Torrie, 1960).
>
> table <- matrix(0, 28, 14)
> pos <- c(10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 50, 100, 200)
> df <- c(2:20, seq(22, 30, by = 2), 40, 60, 100, 1000)
> for (i in 1:28) table [i, ] <- qtukey(p = ((1 - 0.05) ^ (pos - 1)),
> nmeans = pos, df = df[i])
> colnames(table) <- pos
> rownames(table) <- df
>
> Indeed, I get several NaN and some odd figures.
>
> Am I doing something wrong or is there any intrinsic limit for the
> values of p, nmeans and df within the qtukey() function? Just in this
> latter case, is it possible to raise those limits? Some genotype trials
> can have more than thirty treatments under comparison.
>
> I thank you all for your attention.
>
> Andrea Onofri
> Department of Agriculture and Environmental Sciences
> University of Perugia (ITALY)
>
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>
Yes, this looks a bit suspicious, especially the fact that it worsens
with increasing df. The core of the matter seems to be this
> qtukey(log(3.689754e-05),200,1000,log=T)
[1] NaN
Warning messages:
1: In qtukey(log(3.689754e-05), 200, 1000, log = T) :
convergence failed in 'qtukey'
2: In qtukey(p, nranges, nmeans, df, lower.tail, log.p) : NaNs produced
which presumably is from inverting ptukey, but
> f <- Vectorize(function(x)ptukey(x,200,1000,log=T)-log(3.689754e-05))
> curve(f, from=3,to=5)
> uniroot(f, c(3,5))
$root
[1] 3.760707
$f.root
[1] -5.756398e-05
$iter
[1] 6
$estim.prec
[1] 6.103516e-05
is looking perfectly nice.
--
O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907
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