Dear Duncan,
Many thanks for your reply. What you propose is a very good idea yes. However,
I am interested in the coefficients...
Best,
S
________________________________
De : Duncan Murdoch <murdoch.dun...@gmail.com>
Cc : R-help Mailing List <r-help@r-project.org>
Envoyé le : Mercredi 6 juillet 2016 14h13
Objet : Re: [R] BCa Bootstrapped regression coefficients from lmrob function
not working
On 06/07/2016 7:56 AM, varin sacha via R-help wrote:
> Dear Professor Dalgaard,
>
> Okay, this is what I was afraid of.
> Many thanks for your response. I know that my next question is off-topic here
> (on this website) but is it nevertheless possible to calculate confidence
> intervals for MARS regression or CIs for MARS will just be impossible to
> calculate ?
If you think of the fitted curve or surface as the output rather than
the coefficients that define it, it makes sense to get pointwise
confidence intervals, or joint confidence regions for multiple
locations. Just do predictions at a set of fixed locations.
Duncan Murdoch
> Best
> S
>
>
>
> ----- Mail original -----
> De : peter dalgaard <pda...@gmail.com>
> Cc : Bert Gunter <bgunter.4...@gmail.com>; R-help Mailing List
> <r-help@r-project.org>
> Envoyé le : Mercredi 6 juillet 2016 11h05
> Objet : Re: [R] BCa Bootstrapped regression coefficients from lmrob function
> not working
>
> Offhand, I would suspect that the cause is that earth() does not return a
> coefficient vector of the same length in every bootstrap iteration
> (_adaptive_ regression splines). This makes the bootstrap rather tricky to
> even define.
>
> -pd
>
>>
>> Dear Bert,
>>
>> You are right.
>>
>>> results=boot(data=newdata, statistic=boot.MARS, R=1000,formula=PIBparHab ~
>>> QUALITESANSREDONDANCE + competitivite + innovation)
>> Erreur dans boot(data = newdata, statistic = boot.MARS, R = 1000, formula =
>> PIBparHab ~ :
>> le nombre d'objets à remplacer n'est pas multiple de la taille du
>> remplacement
>>
>> In English it would be something like : number of items to replace is not a
>> multiple of replacement length
>>
>>
>> Best
>> S
>>
>> ________________________________
>> De : Bert Gunter <bgunter.4...@gmail.com>
>> Cc : peter dalgaard <pda...@gmail.com>; R-help Mailing List
>> <r-help@r-project.org>
>> Envoyé le : Mercredi 6 juillet 2016 1h19
>> Objet : Re: [R] BCa Bootstrapped regression coefficients from lmrob function
>> not working
>>
>>
>> It would help to show your error message, n'est-ce pas?
>>
>> Cheers,
>> Bert
>>
>>
>> Bert Gunter
>>
>> "The trouble with having an open mind is that people keep coming along
>> and sticking things into it."
>> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>>
>>
>> On Tue, Jul 5, 2016 at 2:51 PM, varin sacha via R-help
>> <r-help@r-project.org> wrote:
>>> Dear Professor Dalgaard,
>>>
>>> I really thank you lots for your response. I have solved my problem. Now, I
>>> have tried to do the same (calculate the BCa bootstrapped CIs) for the MARS
>>> regression, and I get an error message. If somebody has a hint to solve my
>>> problem, would be highly appreciated.
>>>
>>> Reproducible example :
>>>
>>>
>>> Dataset =
>>> data.frame(PIBparHab=c(43931,67524,48348,44827,52409,15245,24453,57636,28992,17102,51495,47243,40908,22494,12784,48391,44221,32514,35132,46679,106022,9817,99635,38678,49128,12876,20732,17151,19670,41053,22488,57134,83295,10660),
>>>
>>> QUALITESANSREDONDANCE=c(1082.5,1066.6,1079.3,1079.9,1074.9,1008.6,1007.5,1111.3,1108.2,1109.7,1059.6,1165.1,1026.7,1035.1,997.8,1044.8,1073.6,1085.7,1083.8,1021.6,1036.2,1075.3,1069.3,1101.4,1086.9,1072.1,1166.7,983.9,1004.5,1082.5,1123.5,1094.9,1105.1,1010.8),
>>>
>>> competitivite=c(89,83,78,73,90,71,77,85,61,67,98,82,70,43,57,78,72,79,61,71,86,63,90,75,87,64,60,56,66,80,53,91,97,62),
>>>
>>> innovation=c(56,52,53,54,57,43,54,60,47,55,58,62,52,35,47,59,56,56,45,52,58,33,57,57,61,40,45,41,50,61,50,65,68,34))
>>>
>>> install.packages("earth")
>>>
>>> library(earth)
>>>
>>> newdata=na.omit(Dataset)
>>>
>>> model=earth(PIBparHab ~ QUALITESANSREDONDANCE + competitivite +
>>> innovation,data=newdata)
>>>
>>> summary(model)
>>>
>>> plot(model)
>>>
>>> plotmo(model)
>>>
>>>
>>> boot.MARS=function(formula,data,indices) {
>>>
>>> d=data[indices,]
>>>
>>> fit=earth(formula,data=d)
>>>
>>> return(coef(fit))
>>>
>>> }
>>>
>>> library(boot)
>>>
>>> results=boot(data=newdata, statistic=boot.MARS, R=1000,formula=PIBparHab ~
>>> QUALITESANSREDONDANCE + competitivite + innovation)
>>>
>>> boot.ci(results, type= "bca",index=2)
>>>
>>>
>>> Best,
>>> S
>>>
>>> ________________________________
>>> De : peter dalgaard <pda...@gmail.com>
>>>
>>> Cc : R-help Mailing List <r-help@r-project.org>
>>> Envoyé le : Dimanche 3 juillet 2016 18h19
>>> Objet : Re: [R] BCa Bootstrapped regression coefficients from lmrob
>>> function not working
>>>
>>>
>>>
>>>> On 03 Jul 2016, at 13:47 , varin sacha via R-help <r-help@r-project.org>
>>>> wrote:
>>>>
>>>> Dear R-experts,
>>>>
>>>> I am trying to calculate the bootstrapped (BCa) regression coefficients
>>>> for a robust regression using MM-type estimator (lmrob function from
>>>> robustbase package).
>>>>
>>>> My R code here below is showing a warning message ([1] "All values of t
>>>> are equal to
>>>> 22.2073014256803\n Can not calculate confidence intervals" NULL), I was
>>>> wondering if it was because I am trying to fit a robust regression with
>>>> lmrob function rather than a simple lm ? I mean maybe the boot.ci function
>>>> does not work with lmrob function ? If not, I was wondering what was going
>>>> on ?
>>>
>>> You need to review your code. You calculate a,b,c,d in the global
>>> environment and create newdata as a subset of Dataset, then use a,b,c,d in
>>> the formula, but no such variables are in newdata. AFAICT, all your
>>> bootstrap fits use the _same_ global values for a,b,c,d hence give the same
>>> result 1000 times...
>>>
>>> -pd
>>>
>>>
>>>
>>>>
>>>> Here is the reproducible example
>>>>
>>>>
>>>> Dataset =
>>>> data.frame(PIBparHab=c(43931,67524,48348,44827,52409,15245,24453,57636,28992,17102,51495,47243,40908,22494,12784,48391,44221,32514,35132,46679,106022,9817,99635,38678,49128,12876,20732,17151,19670,41053,22488,57134,83295,10660),
>>>>
>>>> QUALITESANSREDONDANCE=c(1082.5,1066.6,1079.3,1079.9,1074.9,1008.6,1007.5,1111.3,1108.2,1109.7,1059.6,1165.1,1026.7,1035.1,997.8,1044.8,1073.6,1085.7,1083.8,1021.6,1036.2,1075.3,1069.3,1101.4,1086.9,1072.1,1166.7,983.9,1004.5,1082.5,1123.5,1094.9,1105.1,1010.8),
>>>>
>>>> competitivite=c(89,83,78,73,90,71,77,85,61,67,98,82,70,43,57,78,72,79,61,71,86,63,90,75,87,64,60,56,66,80,53,91,97,62),
>>>>
>>>> innovation=c(56,52,53,54,57,43,54,60,47,55,58,62,52,35,47,59,56,56,45,52,58,33,57,57,61,40,45,41,50,61,50,65,68,34))
>>>>
>>>> library("robustbase")
>>>> newdata=na.omit(Dataset)
>>>> a=Dataset$PIBparHab
>>>> b=Dataset$QUALITESANSREDONDANCE
>>>> c=Dataset$competitivite
>>>> d=Dataset$innovation
>>>>
>>>> fm.lmrob=lmrob(a~b+c+d,data=newdata)
>>>> fm.lmrob
>>>>
>>>> boot.Lmrob=function(formula,data,indices) {
>>>> d=data[indices,]
>>>> fit=lmrob(formula,data=d)
>>>> return(coef(fit))
>>>> }
>>>>
>>>> library(boot)
>>>> results=boot(data=newdata, statistic=boot.Lmrob, R=1000,formula=a~b+c+d)
>>>> boot.ci(results, type= "bca",index=2)
>>>>
>>>>
>>>> Any help would be highly appreciated,
>>>> S
>>>>
>>>> ______________________________________________
>>>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>> PLEASE do read the posting guide
>>>> http://www.R-project.org/posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>> --
>>> Peter Dalgaard, Professor,
>>> Center for Statistics, Copenhagen Business School
>>> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
>>> Phone: (+45)38153501
>>> Office: A 4.23
>>> Email: pd....@cbs.dk Priv: pda...@gmail.com
>
>>
>>>
>>> ______________________________________________
>>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>
______________________________________________
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