[R] Interpretation of davies.test() in segmented package

[Vito Muggeo]

dear Greg,

 > It does not, however give me
 > Pr(>|t|) for the break point coefficients.
Of course a test H_0: breakpoint=0 is meaningless..

 > I need to answer the question
 > H:0 Beta0=Beta with a certainty metric,
sorry, who is your "Beta0"?

davies.test() tests the hypothesis H0: leftSlope=rightSlope which 
implies "diffSlope=0" and then "the breakpoint does not exist". K in 
davies.test() means the number of evaluation points used to compute the 
approximate p-value..

Please contact me off list if you need more details (given detailed 
questions)

vito



Il 16/11/2012 20.57, Greg Cohn ha scritto:
> My data:
> I have raw data points that form a logit style curve as if they were a time
> series. Which is to say they form 3 distinct lines with 3 distinct slopes
> in backwards z pattern.  A certain class of my data looks essentially flat
> to the eye with marginal oscillation. What is important to me is the x
> value at which the state change is occurring, in other words, the break
> point
>
> Use of segmented():
> Segmented does a very good job of capturing the breakpoints and fitting
> three distinct slopes, i.e. linear models. It does not, however give me
> Pr(>|t|) for the break point coefficients. I need to answer the question
> H:0 Beta0=Beta with a certainty metric, i.e. probability statistic. This is
> especially important for my, flat looking data class.
>
> davies.test() question:
> davies.test() only excepts lm() or glm() objects as input. If I run
> segmented to find 1 breakpoint instead of 2, I get a totally bogus answer.
> Without knowing the breakpoints, how is this test able to assess the proper
> breakpiont? It appears to only give 1 best breakpoint, which is not
> consistent with the breakpoints found by segmented(). Also, is K the number
> of breakpoints or the number of iterations that it evaluates the breakpoint?
>
>
> Thanks in advance.
>
>
>
> lmfit<-glm(TotRad_KW~HRRPUA_kWm2,data=d1)
> davies.test(lmfit,seg.Z=~HRRPUA_kWm2,k=1000,alternative="less",
> beta0=0,dispersion=NULL)
>
> Davies' test for a change in the slope
>
> data:  Model =  gaussian , link = identity
> formula = TotRad_KW ~ HRRPUA_kWm2
> segmented variable = HRRPUA_kWm2
> `Best' at = 561.205, n.points = 1000, p-value < 2.2e-16
> alternative hypothesis: less
>
>
>
>
> segments <- segmented(lmfit, seg.Z=~HRRPUA_kWm2,psi=c(475,550))
> summary(segments)
>
> ***Regression Model with Segmented Relationship(s)***
>
> Call:
> segmented.glm(obj = lmfit, seg.Z = ~HRRPUA_kWm2, psi = c(475,
>      550))
>
> Estimated Break-Point(s):
>              Est. St.Err
> psi1.HRRP 430.2  4.087
> psi2.HRRP 484.6  3.077
>
> t value for the gap-variable(s) V:  0 0
>
> Meaningful coefficients of the linear terms:
>              Estimate Std. Error t value Pr(>|t|)
> (Intercept) -38.6993   274.7666  -0.141   0.8891
> HRRPUA_kWm2   1.4297     0.7472   1.914   0.0668 .
> U1.HRRP      42.2884     4.7696   8.866       NA
> U2.HRRP     -40.5897     4.7123  -8.614       NA
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> (Dispersion parameter for gaussian family taken to be 6934.706)
>
> Null     deviance: 70776718  on 31  degrees of freedom
> Residual deviance:   180302  on 26  degrees of freedom
> AIC: 377.19
>
> Convergence attained in 2 iterations with relative change -1.662839e-14
>
>
>
>
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> and provide commented, minimal, self-contained, reproducible code.
>

-- 
====================================
Vito M.R. Muggeo
Dip.to Sc Statist e Matem `Vianelli'
Università di Palermo
viale delle Scienze, edificio 13
90128 Palermo - ITALY
tel: 091 23895240
fax: 091 485726
http://dssm.unipa.it/vmuggeo