Model Segmentation with Recursive Partitioning

October 26, 2014
By

(This article was first published on Yet Another Blog in Statistical Computing » S+/R, and kindly contributed to R-bloggers)

library(party)

df1 <- read.csv("credit_count.csv")
df2 <- df1[df1$CARDHLDR == 1, ]

mdl <- mob(DEFAULT ~ MAJORDRG + MINORDRG + INCOME + OWNRENT | AGE + SELFEMPL, data = df2, family = binomial(), control = mob_control(minsplit = 1000), model = glinearModel)

print(mdl)
#1) AGE <= 22.91667; criterion = 1, statistic = 48.255
#  2)*  weights = 1116 
#Terminal node model
#Binomial GLM with coefficients:
#(Intercept)     MAJORDRG     MINORDRG       INCOME      OWNRENT  
# -0.6651905    0.0633978    0.5182472   -0.0006038    0.3071785  
#
#1) AGE > 22.91667
#  3)*  weights = 9383 
#Terminal node model
#Binomial GLM with coefficients:
#(Intercept)     MAJORDRG     MINORDRG       INCOME      OWNRENT  
# -1.4117010    0.2262091    0.2067880   -0.0003822   -0.2127193  

### TEST FOR STRUCTURAL CHANGE ###
sctest(mdl, node = 1)
#                   AGE    SELFEMPL
#statistic 4.825458e+01 20.88612025
#p.value   1.527484e-07  0.04273836

summary(mdl, node = 2)
#Coefficients:
#              Estimate Std. Error z value Pr(>|z|)    
#(Intercept) -0.6651905  0.2817480  -2.361 0.018229 *  
#MAJORDRG     0.0633978  0.3487305   0.182 0.855743    
#MINORDRG     0.5182472  0.2347656   2.208 0.027278 *  
#INCOME      -0.0006038  0.0001639  -3.685 0.000229 ***
#OWNRENT      0.3071785  0.2028491   1.514 0.129945    

summary(mdl, node = 3)
#Coefficients:
#              Estimate Std. Error z value Pr(>|z|)    
#(Intercept) -1.412e+00  1.002e-01 -14.093  < 2e-16 ***
#MAJORDRG     2.262e-01  7.067e-02   3.201  0.00137 ** 
#MINORDRG     2.068e-01  4.925e-02   4.199 2.68e-05 ***
#INCOME      -3.822e-04  4.186e-05  -9.131  < 2e-16 ***
#OWNRENT     -2.127e-01  7.755e-02  -2.743  0.00609 ** 

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