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Romeo Palijan edited comment on MATH296 at 10/7/09 10:01 AM:

Yes. I think that is correct. No influence on the approximation coefficients but still getting
normalized (getting approximation values).
Here is an except from the userguide for the Rlimma package, which uses the loessFit function
for some operations:
bq.Any spot quality weights found in RG will be used in the normalization by default. This
means for example that spots with zero weight (flagged out) will not influence the normalization
of other spots. The use of spot quality weights will not however result in any spots being
removed from the data object. Even spots with zero weight will be normalized and will appear
in the output object, such spots will simply not have any influence on the other spots.
However I am not sure how to handle the bandwidth. Example: if you have a bandwidth of 0.3,
how do you compute the relevant points?
1. Find the 30% in the complete set and the use only the weighted ones inside this set
2. Look at all weighted ones and use 30% of them
To me the first one sounds like the logical one but I am not sure.
was (Author: romeop):
Yes. I think that is correct. No influence on the approximation coefficients but still
getting normalized (getting approximation values).
Here is an except from the userguide for the Rlimma package, which uses the loessFit function
for some operations:
??Any spot quality weights found in RG will be used in the normalization by default. This
means for example that spots with zero weight (flagged out) will not influence the normalization
of other spots. The use of spot quality weights will not however result in any spots
being removed from the data object. Even spots with zero weight will be normalized and will
appear in the output object, such spots will simply not have any influence on the other spots.??
However I am not sure how to handle the bandwidth. Example: if you have a bandwidth of 0.3,
how do you compute the relevant points?
1. Find the 30% in the complete set and the use only the weighted ones inside this set
2. Look at all weighted ones and use 30% of them
To me the first one sounds like the logical one but I am not sure.
> LoessInterpolator.smooth() not working correctly
> 
>
> Key: MATH296
> URL: https://issues.apache.org/jira/browse/MATH296
> Project: Commons Math
> Issue Type: Bug
> Affects Versions: 2.0
> Environment: Java 1.6 on Vista
> Reporter: Romeo Palijan
> Fix For: 2.1
>
> Attachments: math296test.patch, math296.patch
>
>
> I have been comparing LoessInterpolator.smooth output with the loessFit output from R
(Rproject.org, probably the most widely used loess implementation) and have had strangely
different numbers. I have created a small set to test the difference and something seems to
be wrong with the smooth method but I do no know what and I do not understand the code.
> *Example 1*
> xinput: 1.5 3.0 6 8 1213 22 242831
> yinput: 3.16.13.12.11.45.15.16.17.17.2
> Output LoessInterpolator.smooth():NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
> Output from loessFit() from R: 3.1911780275209743.04072012314740372.70895389037786362.74508232744902974.3880110005495194.600789523818485.29882175871148055.8675363884578986.77977947778797057.444888598397342
> *Example 2 (same xvalues, yvalues just floored)*
> xinput: 1.5 3.0 6 8 1213 22 242831
> yinput: 3632155677
> Output LoessInterpolator.smooth(): 36320.99999999999990055.000000000000170555.99999999999997276.999999999999967
> Output from loessFit() from R: 3.0914239273530682.94115215725242372.609679506755052.74217593222722484.3829969123004424.6467743166325625.2251536585634245.7683019174770156.6370791393130737.270482144410326
> As you see the output is practically the replicated yinput.
> At this point this funtionality is critical for us but I could not find any other suitable
javaimplementation. Help. Maybe this strange behaviour gives someone a clue?

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