Lab2

El alumno deberá realizar los ejercicios 5.3.1, 5.3.2 y 5.3.3 del libro de texto (inicia en página 190). Además deberá realizar el ejercicio 4.6.5 sobre KNN ubicado en la página 163 del libro de texto.

mean((mpg-predict(lm.fit3,Auto))[-train]^2)

[1] 19.2574

5.3.2 LOOCV

cv.error=rep(0,5)
for(i in 1:5){
  glm.fit=glm(mpg~poly(horsepower, i), data=Auto)
  cv.error[i] = cv.glm(Auto, glm.fit)$delta[1]
}
cv.error

[1] 24.23151 19.24821 19.33498 19.42443 19.03321

5.3.3 k-fold Cross-Validation

set.seed(17)
cv.error.10=rep(0,10)
for(i in 1:10){
  glm.fit=glm(mpg~poly(horsepower, i),data=Auto)
  cv.error.10[i]=cv.glm(Auto, glm.fit, K=10)$delta
}

number of items to replace is not a multiple of replacement lengthnumber of items to replace is not a multiple of replacement lengthnumber of items to replace is not a multiple of replacement lengthnumber of items to replace is not a multiple of replacement lengthnumber of items to replace is not a multiple of replacement lengthnumber of items to replace is not a multiple of replacement lengthnumber of items to replace is not a multiple of replacement lengthnumber of items to replace is not a multiple of replacement lengthnumber of items to replace is not a multiple of replacement lengthnumber of items to replace is not a multiple of replacement length

cv.error.10

 [1] 24.20520 19.18924 19.30662 19.33799 18.87911 19.02103 18.89609 19.71201
 [9] 18.95140 19.50196

5.3.4 The Bootstrap

boot(Portfolio, alpha.fn, R=1000)

ORDINARY NONPARAMETRIC BOOTSTRAP


Call:
boot(data = Portfolio, statistic = alpha.fn, R = 1000)


Bootstrap Statistics :
     original     bias    std. error
t1* 0.6596797 0.01211578   0.3073555

summary(lm(mpg~horsepower+I(horsepower^2),data=Auto))$coef

                    Estimate   Std. Error   t value      Pr(>|t|)
(Intercept)     56.900099702 1.8004268063  31.60367 1.740911e-109
horsepower      -0.466189630 0.0311246171 -14.97816  2.289429e-40
I(horsepower^2)  0.001230536 0.0001220759  10.08009  2.196340e-21

4.6.5 KNN

library(class)
library(MASS)
library(ISLR)
names(Smarket)

[1] "Year"      "Lag1"      "Lag2"      "Lag3"      "Lag4"      "Lag5"     
[7] 1250    9

summary(Smarket)

      Year           Lag1                Lag2                Lag3          
 Min.   :2001   Min.   :-4.922000   Min.   :-4.922000   Min.   :-4.922000  
 1st Qu.:2002   1st Qu.:-0.639500   1st Qu.:-0.639500   1st Qu.:-0.640000  
 Median :2003   Median : 0.039000   Median : 0.039000   Median : 0.038500  
 Mean   :2003   Mean   : 0.003834   Mean   : 0.003919   Mean   : 0.001716  
 3rd Qu.:2004   3rd Qu.: 0.596750   3rd Qu.: 0.596750   3rd Qu.: 0.596750  
 Max.   :2005   Max.   : 5.733000   Max.   : 5.733000   Max.   : 5.733000  
      Lag4                Lag5              Volume      
 Min.   :-4.922000   Min.   :-4.92200   Min.   :0.3561  
 1st Qu.:-0.640000   1st Qu.:-0.64000   1st Qu.:1.2574  
 Median : 0.038500   Median : 0.03850   Median :1.4229  
 Mean   : 0.001636   Mean   : 0.00561   Mean   :1.4783  
 3rd Qu.: 0.596750   3rd Qu.: 0.59700   3rd Qu.:1.6417  
 Max.   : 5.733000   Max.   : 5.73300   Max.   :3.1525  
     Today           Direction 
 Min.   :-4.922000   Down:602  
 1st Qu.:-0.639500   Up  :648  
 Median : 0.038500             
 Mean   : 0.003138             
 3rd Qu.: 0.596750             
 Max.   : 5.733000  

attach(Smarket)

The following objects are masked from Smarket (pos = 3):

    Direction, Lag1, Lag2, Lag3, Lag4, Lag5, Today, Volume, Year

The following objects are masked from Smarket (pos = 4):

    Direction, Lag1, Lag2, Lag3, Lag4, Lag5, Today, Volume, Year

The following objects are masked from Smarket (pos = 5):

    Direction, Lag1, Lag2, Lag3, Lag4, Lag5, Today, Volume, Year

train = (Year<2005)
Direction.2005 = Direction[-train]
train.X=cbind(Lag1, Lag2)[train,]
test.X=cbind(Lag1, Lag2)[!train,]
train.Direction = Direction[train]
set.seed(1)
knn.pred=knn(train.X, test.X, train.Direction, k=1)
table(knn.pred, Direction.2005)

Error in table(knn.pred, Direction.2005) : 
  all arguments must have the same length

table(knn.pred, Direction.2005)

Error in table(knn.pred, Direction.2005) : 
  object 'Direction.2005' not found