I had that result sometimes when testing as well. You don't offer any code so
there's nothing I can do to follow-up.
--
David.
On Aug 18, 2014, at 4:56 AM, Jan Stanstrup wrote:
> The knots are deleted anyway ("Deleting unnecessary knots ..."). It seems to
> make no difference.
>
>
>
> On 08/14/2014 06:06 PM, David Winsemius wrote:
>>
>> On Aug 14, 2014, at 7:17 AM, Jan Stanstrup wrote:
>>
>>> Thank you very much for this snippet!
>>>
>>> I used it on my data and indeed it does give intervals which appear quite
>>> realistic (script and data here
>>> https://github.com/stanstrup/retpred_shiny/blob/master/retdb_admin/make_predictions_CI_tests.R).
>>> I also tried getting the intervals with predict.cobs but the methods
>>> available there gave very narrow bands.
>>> The only problem I can see is that the fit tend to be a bit on the smooth
>>> side. See for example the upper interval limits at x = 2 to 3 and x =1.2.
>>> If then I set lambda to something low like 0.05 the band narrows to nearly
>>> nothing when there are few points. For example at x = 2.5. Is there some
>>> other parameter I would be adjusting?
>>>
>>
>> Try specifying the number and location of the knots (using my example data):
>>
>> > Rbs.9 <- cobs(age,analyte,constraint="increase",tau=0.9, nknots=6,
>> > knots=seq(60,85,by=5))
>> > plot(age,analyte, ylim=c(0,2000))
>> > lines(predict(Rbs.9), col = 2, lwd = 1.5)
>>
>> <Mail Attachment.png>
>>
>> --
>> David.
>>
>>>
>>>
>>> ----------------------
>>> Jan Stanstrup
>>> Postdoc
>>>
>>> Metabolomics
>>> Food Quality and Nutrition
>>> Fondazione Edmund Mach
>>>
>>>
>>>
>>> On 08/14/2014 02:02 AM, David Winsemius wrote:
>>>>
>>>> On Aug 12, 2014, at 8:40 AM, Bert Gunter wrote:
>>>>
>>>>> PI's of what? -- future individual values or mean values?
>>>>>
>>>>> I assume quantreg provides quantiles for the latter, not the former.
>>>>> (See ?predict.lm for a terse explanation of the difference).
>>>>
>>>> I probably should have questioned the poster about what was meant by a
>>>> "prediction interval for a monotonic loess curve". I was suggesting
>>>> quantile regression for estimation of a chosen quantile, say the 90th
>>>> percentile. I was thinking it could produce the analogue of a 90th
>>>> percentile value (with no reference to a mean value or use of
>>>> presumed distribution within adjacent windows of say 100-150 points. I had
>>>> experience using the cobs function (in the package of the same name) as
>>>> Koenker illustrates:
>>>>
>>>> age <- runif(1000,min=60,max=85)
>>>>
>>>> analyte <- rlnorm(1000,4*(age/60),age/60)
>>>> plot(age,analyte)
>>>>
>>>> library(cobs)
>>>> library(quantreg)
>>>> Rbs.9 <- cobs(age,analyte, constraint="increase",tau=0.9)
>>>> Rbs.median <- cobs(age,analyte,constraint="increase",tau=0.5)
>>>>
>>>> png("cobs.png"); plot(age,analyte, ylim=c(0,2000))
>>>> lines(predict(Rbs.9), col = "red", lwd = 1.5)
>>>> lines(predict(Rbs.median), col = "blue", lwd = 1.5)
>>>> dev.off()
>>>> <Mail Attachment.png>
>>>>
>>>> -- David
>>>>
>>>>
>>>>> obtainable from bootstrapping but the details depend on what you are
>>>>> prepared to assume. Consult references or your local statistician for
>>>>> help if needed.
>>>>>
>>>>> -- Bert
>>>>>
>>>>> Bert Gunter
>>>>> Genentech Nonclinical Biostatistics
>>>>> (650) 467-7374
>>>>>
>>>>> "Data is not information. Information is not knowledge. And knowledge
>>>>> is certainly not wisdom."
>>>>> Clifford Stoll
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Tue, Aug 12, 2014 at 8:20 AM, David Winsemius <[email protected]>
>>>>> wrote:
>>>>>>
>>>>>> On Aug 12, 2014, at 12:23 AM, Jan Stanstrup wrote:
>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> I am trying to find a way to estimate prediction intervals (PI) for a
>>>>>>> monotonic loess curve using bootstrapping.
>>>>>>>
>>>>>>> At the moment my approach is to use the boot function from the boot
>>>>>>> package to bootstrap my loess model, which consist of loess + monoproc
>>>>>>> from the monoproc package (to force the fit to be monotonic which gives
>>>>>>> me much improved results with my particular data). The output from the
>>>>>>> monoproc package is simply the fitted y values at each x-value.
>>>>>>> I then use boot.ci (again from the boot package) to get confidence
>>>>>>> intervals. The problem is that this gives me confidence intervals (CI)
>>>>>>> for the "fit" (is there a proper way to specify
>>>>>>> this?) and not a prediction interval. The interval is thus way too
>>>>>>> optimistic to give me an idea of the confidence interval of a predicted
>>>>>>> value.
>>>>>>>
>>>>>>> For linear models predict.lm can give PI instead of CI by setting
>>>>>>> interval = "prediction". Further discussion of that here:
>>>>>>> http://stats.stackexchange.com/questions/82603/understanding-the-confidence-band-from-a-polynomial-regression
>>>>>>> http://stats.stackexchange.com/questions/44860/how-to-prediction-intervals-for-linear-regression-via-bootstrapping.
>>>>>>>
>>>>>>> However I don't see a way to do that for boot.ci. Does there exist a
>>>>>>> way to get PIs after bootstrapping? If some sample code is required I
>>>>>>> am more than happy to supply it but I thought the question was general
>>>>>>> enough to be understandable without it.
>>>>>>>
>>>>>>
>>>>>> Why not use the quantreg package to estimate the quantiles of interest
>>>>>> to you? That way you would not be depending on Normal theory assumptions
>>>>>> which you apparently don't trust. I've used it with the `cobs` function
>>>>>> from the package of the same name to implement the monotonic constraint.
>>>>>> I think there is a worked example in the quantreg package, but since I
>>>>>> bought Koenker's book, I may be remembering from there.
>>>>>> --
>>>>>>
>>>>>> David Winsemius
>>>>>> Alameda, CA, USA
>>>>>>
>>>>>> ______________________________________________
>>>>>> [email protected] mailing list
>>>>>> 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.
>>>>
>>>> David Winsemius
>>>> Alameda, CA, USA
>>>>
>>>
>>> <boot2ci_PI.png><cobs.png>
>>
>> David Winsemius
>> Alameda, CA, USA
>>
>
David Winsemius
Alameda, CA, USA
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