[R] help interpreting a model summary

[zozio32]

Hello, I am all new here.
Thanks for the job done, R really helped me in my thesis lately. However, I
am kind of new in statistics, coming from mecanical engineering, and I
mostly teached myself with "The R Book", so I may do silly things some time.
PLease tell me if you think so.

Anyway, I've just build up a piecewise linear model to fit some data,
including some interaction and i am not sure of how to interpret the
summary:.
here it is:

--------------------------------------------------------------------------------
Call:
lm(formula = weightedDiff ~ angleNoise + (reflection < Break[xMin]) * 
    reflection + (reflection >= Break[xMin]) * reflection +
angleNoise:(reflection < 
    Break[xMin]) * reflection + angleNoise:(reflection >= Break[xMin]) *
reflection)

Residuals:
       Min         1Q     Median         3Q        Max 
-1.073e-03 -1.749e-04 -5.913e-06  1.650e-04  1.311e-03 

Coefficients: (4 not defined because of singularities)
                                                      Estimate Std. Error
(Intercept)                                          0.0134798  0.0001086
angleNoise                                           0.0004658  0.0002245
reflection < Break[xMin]TRUE                        -0.0028766  0.0001236
reflection                                           0.0316122  0.0014741
reflection >= Break[xMin]TRUE                               NA         NA
reflection < Break[xMin]TRUE:reflection              0.0683631  0.0027668
reflection:reflection >= Break[xMin]TRUE                    NA         NA
angleNoise:reflection < Break[xMin]TRUE              0.0011158  0.0002548
angleNoise:reflection >= Break[xMin]TRUE                    NA         NA
angleNoise:reflection < Break[xMin]FALSE:reflection -0.0055751  0.0030620
angleNoise:reflection < Break[xMin]TRUE:reflection  -0.0343745  0.0049164
angleNoise:reflection:reflection >= Break[xMin]TRUE         NA         NA
                                                                                                          
t value       Pr(>|t|)    
(Intercept)                                                                                      
124.079  < 2e-16   ***
angleNoise                                                                                   
2.075       0.0384    *  
reflection < Break[xMin]TRUE                                                   
-23.265   < 2e-16  ***
reflection                                                                                        
21.445    < 2e-16  ***
reflection >= Break[xMin]TRUE                                                
NA             NA    
reflection < Break[xMin]TRUE:reflection                                 
24.708   < 2e-16    ***
reflection:reflection >= Break[xMin]TRUE                               NA             
NA    
angleNoise:reflection < Break[xMin]TRUE                             4.379     
1.41e-05  ***
angleNoise:reflection >= Break[xMin]TRUE                           NA             
NA    
angleNoise:reflection < Break[xMin]FALSE:reflection          -1.821     
0.0692    .  
angleNoise:reflection < Break[xMin]TRUE:reflection            -6.992   
7.35e-12  ***
angleNoise:reflection:reflection >= Break[xMin]TRUE          NA            
NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 0.0002885 on 592 degrees of freedom
Multiple R-squared: 0.9666,	Adjusted R-squared: 0.9662 
F-statistic:  2450 on 7 and 592 DF,  p-value: < 2.2e-16 

--------------------------------------------------------------------------------------------
Basically, I am really not sure of the meaning of this parameter:
 angleNoise:reflection < Break[xMin]FALSE:reflection

Overall, my interpretation is that reflection is important , angle Noise
also but specially when reflection is below the breaking point. Is that
correct?

well, sorry for the first long post

thanks in advance

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