[R] Resistant Local Regression ???
Maciej.Hoffman-Wecker@evotecoai.com
Maciej.Hoffman-Wecker at evotecoai.com
Tue Feb 19 17:27:38 CET 2002
Dear R-users & developers,
i'd like to know your opinion on the following suggestion.
In short:
Wouldn't it be possible to combine the properties of
local regression (loess) and resistant regression (lqs)
in order to cope with fitting of partially continuous data?
I think the lqs-fitting could detect and eliminate
the 2 outliers but perserve the discontinuity at t1
in case of the following data (hope its still courier).
--------------------------------------------------
| |
| * |
| |
| |
| * |
| * * * * * |
| * * |
| * * |
| * |
| |
| |
| |
| |
| |
| * * |
| * * * * * * * |
| * * * * * * * * |
-------|---------|-------------|------------------
t0 t1 t2
Actually i'm interested in fitting a IR^2 -> IR
function. This is why i can't use lowess. (I guess
I shouldn't anyway - BTW, in a "lowess vs. loess"
mail Martin Maechler mentioned the robustness
properties of lowess ("huberizing"). Is lowess
more robust than loess with family=symmetric?.)
I will try to implement this in R-language,
in order to see, whether it works or not.
I hope to hear some opinion or recommendation.
Thanks in advance
maciej
Maciej Hoffman-Wecker
EVOTEC OAI
Screening Operations/Discovery Informatics
Schnackenburgallee 114
D-22525 Hamburg
Germany
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