[R] Prediction intervals (i.e. not CI of the fit) for monotonic loess curve using bootstrapping
Jan Stanstrup
jan.stanstrup at fmach.it
Tue Aug 12 09:23:18 CEST 2014
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.
Any hints are highly appreciated.
----------------------
Jan Stanstrup
Postdoc
Metabolomics
Food Quality and Nutrition
Fondazione Edmund Mach
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