segmented regression

Data loaded

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library(segmented)
homework<-read.table("clipboard",header = TRUE)

Find out the segmented correaltions

通过画出所有变量的散点关系图，找出需要进行分段回归的关系

plot(homework)

Segmented regression

通过上图可以看出，x3和y1之间存在分段关系

plot(homework$x3~homework$y1)

obj<-glm(x3~y1,data = homework)
obj.seg<-segmented(obj,seg.Z = ~y1,psi = 380)
obj

## 
## Call:  glm(formula = x3 ~ y1, data = homework)
## 
## Coefficients:
## (Intercept)           y1  
##    343.8339      -0.2574  
## 
## Degrees of Freedom: 178 Total (i.e. Null);  177 Residual
## Null Deviance:       1689000 
## Residual Deviance: 1111000   AIC: 2077

obj.seg

## Call: segmented.glm(obj = obj, seg.Z = ~y1, psi = 380)
## 
## Meaningful coefficients of the linear terms:
## (Intercept)           y1        U1.y1  
##    530.0537      -0.8793       0.8136  
## 
## Estimated Break-Point(s):
## psi1.y1  
##   388.4  
## 
## Degrees of Freedom: 178 Total (i.e. Null);  175 Residual
## Null Deviance:     1689000 
## Residual Deviance: 808500      AIC: 2024

Plot strategy

par(mfrow=c(2,1))
plot(obj.seg,conf.level = 0.95,shade = TRUE)

## Warning in class(x) == c("segmented", "Arima"): 长的对象长度不是短的对象长
## 度的整倍数

## Warning in class(x) == c("segmented", "Arima"): 长的对象长度不是短的对象长
## 度的整倍数

plot(homework$x3~homework$y1)
plot(obj.seg,add=TRUE,link=FALSE,lwd=2,col=2:3, lty=c(1,3))

## Warning in class(x) == c("segmented", "Arima"): 长的对象长度不是短的对象长
## 度的整倍数

## Warning in class(x) == c("segmented", "Arima"): 长的对象长度不是短的对象长
## 度的整倍数

lines(obj.seg,col=2,pch=19,bottom=FALSE,lwd=2)