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suppressWarnings(library(rpart))
suppressWarnings(library(rpart.plot))
suppressWarnings(library(ISLR))
suppressWarnings(library(knitr))
suppressWarnings(library(caret))

挼㸷攼㸰攼㸳昼㸰昼㸳攼㸷攼㹡攼㸰 昼㸲昼㸰攼㸵攼㸱昼㸳攼㸵攼㹣攼㹥攼㸳攼㹥 攼㹦攼㸰攼㹡攼㸵昼㸲攼㸰: lattice
挼㸷攼㸰攼㸳昼㸰昼㸳攼㸷攼㹡攼㸰 昼㸲昼㸰攼㸵攼㸱昼㸳攼㸵攼㹣攼㹥攼㸳攼㹥 攼㹦攼㸰攼㹡攼㸵昼㸲攼㸰: ggplot2
Registered S3 method overwritten by 'dplyr':
  method           from
  print.rowwise_df     
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

suppressWarnings(library(MASS))

data <- read.table("data\\test_sample.csv", header=T, sep=",")

set.seed(1)
fitRTREE <- rpart(Y~X1+X2+X3, data=data, method='poisson', control=rpart.control(cp = 0))
best.CP = fitRTREE$cptable[which.min(fitRTREE$cptable[,"xerror"]),"CP"]

set.seed(1)
prunedTree <- prune(fitRTREE, cp = best.CP)
printcp(prunedTree)

Rates regression tree:
rpart(formula = Y ~ X1 + X2 + X3, data = data, method = "poisson", 
    control = rpart.control(cp = 0))

Variables actually used in tree construction:
character(0)

Root node error: 3786.9/1000 = 3.7869

n= 1000 

        CP nsplit rel error xerror     xstd
1 0.013395      0         1 1.0089 0.088701

set.seed(1)
glmnbfit <- glm.nb(Y~X1+X2+X3, data=data)

rmse <- function(x) sqrt(mean(x^2))

treeRmse<-rmse(resid(prunedTree))

glmnbfitRmse<-rmse(glmnbfit$residuals)

set.seed(1)
predict(prunedTree, newdata=data.frame(Y=0, X1=4, X2=4, X3=4))

[1] 1.604

set.seed(1)
predict(glmnbfit, newdata=data.frame(Y=0, X1=4, X2=4, X3=4))

        1 
0.2818191 

pred0Tree<-dpois(0,lambda=predict(prunedTree, newdata=data.frame(Y=0, X1=4, X2=4, X3=4)))

mu<-predict(glmnbfit,data.frame(Y=0, X1=4, X2=4, X3=4),type="response")
pred0glmnb<-dnbinom(0,mu=mu,size=glmnbfit$theta)

glmnbfitRmse

[1] 2.131581

treeRmse

[1] 3.423621

pred0glmnb

[1] 0.5262645

pred0Tree

[1] 0.2010905

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