hw02

#4.7.1
library(ISLR2)
names(Smarket)
dim(Smarket)
summary(Smarket)
pairs(Smarket)
cor(Smarket[,-9])
attach(Smarket)
plot(Volume)
#4.7.2
glm.fits <- glm(Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Lag5 + Volume, data = Smarket, family = binomial)
summary(glm.fits)
coef(glm.fits)
summary(glm.fits)$coef
summary(glm.fits)$coef[,4]
glm.probs <- predict(glm.fits, type = "response")
glm.probs[1:10]
contrasts(Direction)
glm.pred <- rep("Down",1250)
glm.pred [glm.probs>.5] = "Up"
table(glm.pred,Direction)
mean(glm.pred==Direction)
#
train <- (Year<2005)
Smarket.2005 <- Smarket[!train, ]
dim(Smarket.2005)
Direction.2005 <- Direction[!train]
glm.fits <- glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume,data = Smarket,family = binomial,subset = train)
glm.probs <- predict(glm.fits, Smarket.2005,type = "response")
glm.pred <- rep("Down",252)
glm.pred[glm.probs>.5] <- "Up"
table(glm.pred,Direction.2005)
mean(glm.pred == Direction.2005)
mean(glm.pred != Direction.2005)
#
glm.fits <- glm(Direction~Lag1+Lag2,data = Smarket,family = binomial,subset = train)
glm.probs<- predict(glm.fits,Smarket.2005, type = "response")
glm.pred <- rep("Down", 252)
glm.pred[glm.probs > .5] <- "Up"
table(glm.pred,Direction.2005)
mean(glm.pred == Direction.2005)
#
predict(glm.fits,newdata = data.frame(Lag1=c(1.2,1.5), Lag2=c(1.1,-0.8)), type = "response")