---
layout: post
title: "ISL: Chapter 4"
published: yes
---

## Bài 10

```{r 10}
library(ISLR)
names(Weekly)
dim(Weekly)
summary(Weekly)
pairs(Weekly)
cor(Weekly[,-9])
attach(Weekly)
plot(Volume)

# Logistic regression
glmout=glm(Direction~.-Year-Today,data=Weekly,family=binomial)
summary(glmout)
glmpro=predict(glmout,type="response")
contrasts(Direction)
glmpre=rep("Down",1089)
glmpre[glmpro>.5]="Up"
table(glmpre,Direction)
mean(glmpre==Direction)
(172+427)/1089
427/(427+312)

# split training data
train=(Year<2008)
Weekly0810=Weekly[!train,]
dim(Weekly0810)
Direction0810=Direction[!train]

# using Logistic Regression
glmout2=glm(Direction~Lag2,data=Weekly,family=binomial,subset=train)
glmpro2=predict(glmout2,Weekly0810,type="response")
glmpre2=rep("Down",156)
glmpre2[glmpro2>.5]="Up"
table(glmpre2,Direction0810)
mean(glmpre2==Direction0810)

# using LDA
library(MASS)
ldaout=lda(Direction~Lag2,data=Weekly,subset=train)
ldapro=predict(ldaout,Weekly0810)
ldapre=ldapro$class
table(ldapre,Direction0810)
mean(ldapre==Direction0810)

# using QDA
qdaout=qda(Direction~Lag2,data=Weekly,subset=train)
qdapro=predict(qdaout,Weekly0810)
qdapre=qdapro$class
table(qdapre,Direction0810)
mean(qdapre==Direction0810)

# using KNN, K=1
library(class)
trainK=matrix(Lag2[train])
testK=matrix(Lag2[!train])
trainD=Direction[train]
set.seed(1)
knnpre=knn(trainK,testK,trainD,k=1)
table(knnpre,Direction0810)
mean(knnpre==Direction0810)

# K=3
knnpre3=knn(trainK,testK,trainD,k=3)
table(knnpre3,Direction0810)
mean(knnpre3==Direction0810)

# K=7
knnpre7=knn(trainK,testK,trainD,k=7)
table(knnpre7,Direction0810)
mean(knnpre7==Direction0810)
```

## Bài 11
```{r autodata}
auto=read.csv("./ISL/Auto.csv",na.strings="?")
auto=na.omit(auto)
names(auto)
dim(auto)
summary(auto)
summary(auto$mpg)
mpg.medi=summary(auto$mpg)["Median"]
mpg01=rep(0,392)
mpg01[auto$mpg>mpg.medi]=1
sum(mpg01)
auto01=data.frame(auto,mpg01)
pairs(auto01)

# split training data
trn=(auto01$year<80)
sum(trn)
auto80=auto01[!trn,]
mpg80=auto01$mpg01[!trn]

# perform LDA
library(MASS)
ldaout=lda(mpg01~horsepower+weight+acceleration,data=auto01,subset=trn)
ldaout
ldapro=predict(ldaout,auto80)
ldapre=ldapro$class
table(ldapre,mpg80)
mean(ldapre==mpg80)

# perform QDA
qdaout=qda(mpg01~horsepower+weight+acceleration,data=auto01,subset=trn)
qdapro=predict(qdaout,auto80)
qdapre=qdapro$class
table(qdapre,mpg80)
mean(qdapre==mpg80)

# perform logistic regression
glmout=glm(mpg01~horsepower+weight+acceleration,data=auto01,subset=trn,family=binomial)
summary(glmout)
glmpro=predict(glmout,auto80,type="response")
glmpre=rep(0,sum(!trn))
glmpre[glmpro>.5]=1
sum(glmpre)
table(glmpre,mpg80)
mean(glmpre==mpg80)

# perform KNN
library(class)
attach(auto01)
trnK=cbind(horsepower,weight,acceleration)[trn,]
tstK=cbind(horsepower,weight,acceleration)[!trn,]
trnD=mpg01[trn]
set.seed(1)

# K=1
knnpre=knn(trnK,tstK,trnD,k=1)
table(knnpre,mpg80)
mean(knnpre==mpg80)

# K=3
knnpre=knn(trnK,tstK,trnD,k=3)
table(knnpre,mpg80)
mean(knnpre==mpg80)

# K=5
knnpre=knn(trnK,tstK,trnD,k=7)
table(knnpre,mpg80)
mean(knnpre==mpg80)
```

## Bài 12
```{r writing functions}
Power2=function(x,a){
 print(x^a) 
}
Power2(2,3)

Power3=function(x,a){
  result=x^a
  return(result)
}

Power2(3,8)
plot(1:10,y=Power3(1:10,2),col=2,xlab="x",ylab="y=x^2")
plot(1:10,y=Power3(1:10,2),col=2,xlab="x",ylab="y=x^2",log="x")
plot(1:10,y=Power3(1:10,2),col=2,xlab="x",ylab="y=x^2",log="y")
plot(1:10,y=Power3(1:10,2),col=2,xlab="x",ylab="y=x^2",log="xy")

PlotPower=function(x,a){
  plot(x,Power3(x,a),xlab="x",ylab=paste("x^",a))
}
PlotPower(1:10,1.7)
```