Dear all,
Here comes a reproducible example, with the original data added.
The error message when running boot() is:
Error in gnls(response.variable ~ a * LD/(b + LD), params - list(a + :
Step halving factor reduced below minimum in NLS step
boot() in this case only seems to work for very small values of R (say, within [1..5]), which is of
course not desirable. Maybe this is due to problems with model convergence.
I would very much appreciate any help.
###
LD=c(4.087462841,2.321928095,4.087462841,1,1,4.087462841,2.321928095,1,1,2.321928095,4.087462841,2.321928095,5.930737338,2.321928095,5.930737338,1,1,2.321928095,2.321928095,4.087462841,1,1,2.321928095,4.087462841,4.087462841,1,2.321928095,1,4.087462841,2.321928095,1,2.321928095,5.930737338,4.087462841,1,4.087462841,2.321928095,4.087462841,5.930737338,4.087462841,1,2.321928095,2.321928095,1,2.321928095,1,1,4.087462841,4.087462841,2.321928095)
L=c(1,1,0,1,0,0,1,0,0,0,1,0,1,1,1,0,0,1,0,0,0,1,1,1,1,0,1,0,0,0,1,0,1,1,0,1,1,1,1,0,0,1,1,1,1,0,0,1,1,0)
response.variable=c(0.335737179487179,0.391025641025641,0.391826923076923,0.487980769230769,0.294070512820513,0.507211538461538,0.3958333,0.0825320512820513,0.443108974358974,0.290064102564103,0.59775641025641,0.514423076923077,0.65625,0.193910256410256,0.4479167,0,0.2604167,0.407852564102564,0.44150641025641,0.596153846153846,0.0600961538461538,0.21875,0.256410256410256,0.3645833,0.435096153846154,0.0793269230769231,0.249198717948718,0.0304487179487179,0.230769230769231,0.485576923076923,0.684294871794872,0.0737179487179487,0.490384615384615,0.599358974358974,0.215544871794872,0.219551282051282,0.602564102564103,0.907852564102564,0.391025641025641,0.43349358974359,0.0384615384615385,0.337339743589744,0.502403846153846,0.405448717948718,1,0.362980769230769,0.116185897435897,0.459134615384615,0.661057692307692,0.0769230769230769)
a=data.frame(LD,L,response.variable)
model=gnls(model = response.variable ~ a * LD/(b + LD), data = a,
params = list(a + b ~ L), start = c(1, 1, 1, 1))
df-cbind(a,fit-as.numeric(predict(model,list(LD=1,L=0.5
rs-scale(resid(model),scale=F)
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)))
}
(model.boot-boot(rs,model.bootfunc,R=100))
booted-boot.ci(model.boot,index=1,type=c(norm))
booted$t0
booted$normal
###
Best wishes,
Christoph.
Prof Brian Ripley schrieb:
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 biasstd. 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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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
