require(ISLR)
names(Smarket)
summary(Smarket)
?Smarket
pairs(Smarket,col=Smarket$Direction)
# Logistic regression
glm.fit=glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume,
            data=Smarket,family=binomial)
summary(glm.fit)
glm.probs=predict(glm.fit,type="response") 
glm.probs[1:5]
glm.pred=ifelse(glm.probs>0.5,"Up","Down")
attach(Smarket)
table(glm.pred,Direction)
mean(glm.pred==Direction)
# Make training and test set
train = Year<2005
glm.fit=glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume,
            data=Smarket,family=binomial, subset=train)
glm.probs=predict(glm.fit,newdata=Smarket[!train,],type="response") 
glm.pred=ifelse(glm.probs >0.5,"Up","Down")
Direction.2005=Smarket$Direction[!train]
table(glm.pred,Direction.2005)
mean(glm.pred==Direction.2005)
#Fit smaller model
glm.fit=glm(Direction~Lag1+Lag2,
            data=Smarket,family=binomial, subset=train)
glm.probs=predict(glm.fit,newdata=Smarket[!train,],type="response") 
glm.pred=ifelse(glm.probs >0.5,"Up","Down")
table(glm.pred,Direction.2005)
mean(glm.pred==Direction.2005)
106/(76+106)


require(MASS)

## Linear Discriminant Analysis
lda.fit=lda(Direction~Lag1+Lag2,data=Smarket, subset=Year<2005)
lda.fit
plot(lda.fit)
Smarket.2005=subset(Smarket,Year==2005)
lda.pred=predict(lda.fit,Smarket.2005)
lda.pred[1:5,]
class(lda.pred)
data.frame(lda.pred)[1:5,]
table(lda.pred$class,Smarket.2005$Direction)
mean(lda.pred$class==Smarket.2005$Direction)

## K-Nearest Neighbors
library(class)
?knn
attach(Smarket)
Xlag=cbind(Lag1,Lag2)
train=Year<2005
knn.pred=knn(Xlag[train,],Xlag[!train,],Direction[train],k=1)
table(knn.pred,Direction[!train])
mean(knn.pred==Direction[!train])