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 [[alternative HTML version deleted]]
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