require(ISLR)
## Loading required package: ISLR
## Warning: package 'ISLR' was built under R version 3.1.2
names(Smarket)
## [1] "Year" "Lag1" "Lag2" "Lag3" "Lag4" "Lag5"
## [7] "Volume" "Today" "Direction"
summary(Smarket)
## Year Lag1 Lag2
## Min. :2001 Min. :-4.922000 Min. :-4.922000
## 1st Qu.:2002 1st Qu.:-0.639500 1st Qu.:-0.639500
## Median :2003 Median : 0.039000 Median : 0.039000
## Mean :2003 Mean : 0.003834 Mean : 0.003919
## 3rd Qu.:2004 3rd Qu.: 0.596750 3rd Qu.: 0.596750
## Max. :2005 Max. : 5.733000 Max. : 5.733000
## Lag3 Lag4 Lag5
## Min. :-4.922000 Min. :-4.922000 Min. :-4.92200
## 1st Qu.:-0.640000 1st Qu.:-0.640000 1st Qu.:-0.64000
## Median : 0.038500 Median : 0.038500 Median : 0.03850
## Mean : 0.001716 Mean : 0.001636 Mean : 0.00561
## 3rd Qu.: 0.596750 3rd Qu.: 0.596750 3rd Qu.: 0.59700
## Max. : 5.733000 Max. : 5.733000 Max. : 5.73300
## Volume Today Direction
## Min. :0.3561 Min. :-4.922000 Down:602
## 1st Qu.:1.2574 1st Qu.:-0.639500 Up :648
## Median :1.4229 Median : 0.038500
## Mean :1.4783 Mean : 0.003138
## 3rd Qu.:1.6417 3rd Qu.: 0.596750
## Max. :3.1525 Max. : 5.733000
?Smarket
## starting httpd help server ... done
pairs(Smarket,col=Smarket$Direction)
glm.fit<- span=""> glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume,data=Smarket,family=binomial)
summary(glm.fit)
##
## Call:
## glm(formula = Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Lag5 +
## Volume, family = binomial, data = Smarket)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.446 -1.203 1.065 1.145 1.326
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.126000 0.240736 -0.523 0.601
## Lag1 -0.073074 0.050167 -1.457 0.145
## Lag2 -0.042301 0.050086 -0.845 0.398
## Lag3 0.011085 0.049939 0.222 0.824
## Lag4 0.009359 0.049974 0.187 0.851
## Lag5 0.010313 0.049511 0.208 0.835
## Volume 0.135441 0.158360 0.855 0.392
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1731.2 on 1249 degrees of freedom
## Residual deviance: 1727.6 on 1243 degrees of freedom
## AIC: 1741.6
##
## Number of Fisher Scoring iterations: 3
glm.probs<- span=""> predict(glm.fit,type='response')
summary(glm.probs)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4084 0.5020 0.5180 0.5184 0.5338 0.6486
glm.probs[1:5]
## 1 2 3 4 5
## 0.5070841 0.4814679 0.4811388 0.5152224 0.5107812
attach(Smarket)
glm.pred<- span=""> ifelse(glm.probs>.5,'Up','Down')
table(glm.pred,Direction)
## Direction
## glm.pred Down Up
## Down 145 141
## Up 457 507
mean(glm.pred==Direction)
## [1] 0.5216
train<- span=""> Year<2005
glm.fit=glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume,
data=Smarket,family=binomial,subset=train)
glm.probs<- span=""> predict(glm.fit,newdata=Smarket[!train,],type='response')
glm.pred<- span=""> ifelse(glm.probs>.5,'Up','Down')
Direction.2005<- span=""> Smarket$Direction[!train]
table(glm.pred,Direction.2005)
## Direction.2005
## glm.pred Down Up
## Down 77 97
## Up 34 44
mean(glm.pred==Direction.2005)
## [1] 0.4801587
glm.git<- span=""> glm(Direction~Lag1+Lag2,data=Smarket,family=binomial,subset=train)
glm.probs<- span=""> predict(glm.fit,newdata=Smarket[!train,],type='response')
glm.pred<- span=""> ifelse(glm.probs>.5,"Up","Down")
table(glm.pred,Direction.2005)
## Direction.2005
## glm.pred Down Up
## Down 77 97
## Up 34 44
mean(glm.pred==Direction.2005)
## [1] 0.4801587
106/(76+106)
## [1] 0.5824176
