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Park, Kyong H Mr. RDECOM

2005-Jun-08 12:25 UTC

[R] Robustness of Segmented Regression Contributed by Muggeo

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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roger koenker

2005-Jun-08 12:36 UTC

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[R] Robustness of Segmented Regression Contributed by Muggeo

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.
>
>
>
>
>     [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide! http://www.R-project.org/posting- 
> guide.html
>
>

Achim Zeileis

2005-Jun-08 12:55 UTC

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[R] Robustness of Segmented Regression Contributed by Muggeo

On Wed, 8 Jun 2005 08:25:16 -0400  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.
When you keep the number of break points fixed, then there is a unique
solution to the problem of fitting a segmented regression: the solution
which maximizes the likelihood (or for linear models equivalently
minimizes the RSS). Vito's segmented package gives an iterative method
which can be shown to converge to this unique solution. If empirically
you find different solutions with different starting values, you can
always compare them using the RSS or log-likelihood and choose the one
which fits better (because the other one can't be the optimal solution).

The function breakpoints() in package strucchange computes (as
opposed to approximates) the unique solution for a fully segmented model
instead of a broken line trend.

Another nonparametric solution using quantreg was already pointed out by
Roger.

hth,
Z
 > 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. 
> 
> 
> 
> 
> 	[[alternative HTML version deleted]]
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
>

R help - Jun 2005 - Robustness of Segmented Regression Contributed by Muggeo

[R] Robustness of Segmented Regression Contributed by Muggeo

[R] Robustness of Segmented Regression Contributed by Muggeo

[R] Robustness of Segmented Regression Contributed by Muggeo

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