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


-- 
Greg Cohn
Forestry Technician
USDA Forest Service Fire Lab

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