On Fri, 7 Nov 2008, Christoph Scherber wrote:
Dear all,
I am trying to bootstrap predictions from gnls models using the following
code:
# a is the dataframe with which I am working; it contains the variables
# response.variable,LD,L,G,P and F
And without it your code is not reproducible.
###
model=gnls(response.variable ~ a * LD/(b + LD),
params = list(a + b ~ L), start = c(1,1,1,1), data=a)
df=cbind(a,fit=predict(model,list(LD=1,L=0.5,G=0.5,P=0.46,F=2.2)))
model.bootfunc=function(rs,i){
df$response.variable=df$fit+rs[i]
as.numeric(predict(gnls(response.variable ~ a * LD/(b + LD),
params = list(a + b ~ L), start = coef(model), data=df)))
}
rs=scale(resid(model),scale=F)
(model.boot=boot(rs,model.bootfunc,R=1))
booted=boot.ci(model.boot,index=1,type=c("norm","basic","perc","bca"))
Do please try to make your code readable, using spaces and <- for
assignments. I would have spotted the problem much sooner with legible,
reproducible code.
###
The problem is that this code yields "NA" for the s.e. of the bootstrap
statistics:
Bootstrap Statistics :
original bias std. error
t1* 0.1651658 -0.020663364 NA
t2* 0.1669592 -0.021759335 NA
t3* 0.1676765 -0.001858686 NA
t4* 0.1726982 -0.025321349 NA
t5* 0.1658092 0.024721214 NA
And hence the boot.ci function and others don?t work.
Does anyone have an idea on that?
Yes: how can you estimate standard errors from a single sample (you set
R=1)?
Many thanks and best wishes
Christoph
--
Dr. rer.nat. Christoph Scherber
University of Goettingen
DNPW, Agroecology
Waldweg 26
D-37073 Goettingen
Germany
phone +49 (0)551 39 8807
fax +49 (0)551 39 8806
Homepage http://www.gwdg.de/~cscherb1
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--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
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______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.