You might try rqss() in the quantreg package. It gives piecewise
linear fits
for a nonparametric form of median regression using total variation
of the
derivative of the fitted function as a penalty term. A tuning parameter
(lambda) controls the number of distinct segments. More details are
available in the vignette for the quantreg package.
url: www.econ.uiuc.edu/~roger Roger Koenker
email [EMAIL PROTECTED] Department of Economics
vox: 217-333-4558 University of Illinois
fax: 217-244-6678 Champaign, IL 61820
On Jun 8, 2005, at 7:25 AM, Park, Kyong H Mr. RDECOM wrote:
Hello, R users,
I applied segmented regression method contributed by Muggeo and got
different slope estimates depending on the initial break points.
The results
are listed below and I'd like to know what is a reasonable approach
handling
this kinds of problem. I think applying various initial break
points is
certainly not a efficient approach. Is there any other methods to
deal with
segmented regression? From a graph, v shapes are more clear at 1.2
and 1.5
break points than 1.5 and 1.7. Appreciate your help.
Result1:
Initial break points are 1.2 and 1.5. The estimated break points
and slopes:
Estimated Break-Point(s):
Est. St.Err
Mean.Vel 1.285 0.05258
1.652 0.01247
Est. St.Err. t value CI
(95%).l
CI(95%).u
slope1 0.4248705 0.3027957 1.403159 -0.1685982
1.018339
slope2 2.3281445 0.3079903 7.559149 1.7244946
2.931794
slope3 9.5425516 0.7554035 12.632390 8.0619879
11.023115
Adjusted R-squared: 0.9924.
Result2:
Initial break points are 1.5 and 1.7. The estimated break points
and slopes:
Estimated Break-Point(s):
Est. St.Err
Mean.Vel 1.412 0.02195
1.699 0.01001
Est. St.Err. t value CI
(95%).l
CI(95%).u
slope1 0.7300483 0.1381587 5.284129 0.4592623
1.000834
slope2 3.4479466 0.2442530 14.116289 2.9692194
3.926674
slope3 12.5000000 1.7783840 7.028853 9.0144314
15.985569
Adjusted R-squared: 0.995.
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