[R] Robustness of Segmented Regression Contributed by Muggeo

[roger koenker]

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    rkoenker at uiuc.edu            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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>
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