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7.8 Lab: Non-linear Modeling 287 250). Each plot displays the fitted function and pointwise standard errors. The first function is linear in year, the second function a smoothing spline with five degrees of freedom in age, andthethirda step function for education. There are very wide standard errors for the first level attach(Wage)
288 7. Moving Beyond Linearity HS Coll f1(year) −4 −2 0 2 4 f2(age) −8 −6 −4 −2 0 2 f3(education) −4 −2 0 2 4 2003 2005 2007 2009 year 20 30 40 50 60 70 80 age education FIGURE 7.14. The same model is fit as in Figure 7.13, this time excluding the observations for which education is fit=lm(wage∼poly(age ,4),data=Wage) > coef(summary(fit)) Estimate Std. Error t value Pr(>|t|) (Intercept ) 111.704 0.729 153.28 |t|) (Intercept ) -1.84e+02 6.00e+01 -3.07 0.002180 poly(age, 4, raw = T)1 2.12e+01 5.89e+00 3.61 0.000312 poly(age, 4, raw = T)2 -5.64e-01 2.06e-01 -2.74 0.006261
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Springer Texts in Statistics Gareth
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Gareth James • Daniela Witten •
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To our parents: Alison and Michael
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viii Preface disciplines who wish t
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x Contents 3 Linear Regression 59 3
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xii Contents 6.6 Lab 2: Ridge Regre
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xiv Contents 10.5 Lab 2: Clustering
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2 1. Introduction Wage 50 100 200 3
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4 1. Introduction Predicted Probabi
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6 1. Introduction of least squares,
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8 1. Introduction in order to contr
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10 1. Introduction In general, we w
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12 1. Introduction of A and B is de
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14 1. Introduction Name Auto Boston
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16 2. Statistical Learning Sales 5
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18 2. Statistical Learning Income Y
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20 2. Statistical Learning In this
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26 2. Statistical Learning additive
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28 2. Statistical Learning tistical
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30 2. Statistical Learning where ˆ
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32 2. Statistical Learning We now m
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34 2. Statistical Learning Y −10
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36 2. Statistical Learning 0.0 0.5
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38 2. Statistical Learning o o o o
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40 2. Statistical Learning o o o o
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42 2. Statistical Learning Error Ra
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44 2. Statistical Learning The matr
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46 2. Statistical Learning We will
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48 2. Statistical Learning [3,] 3 7
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50 2. Statistical Learning > plot(c
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52 2. Statistical Learning 2.4 Exer
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54 2. Statistical Learning (b) What
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56 2. Statistical Learning 9. This
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3 Linear Regression This chapter is
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3.1 Simple Linear Regression 3.1 Si
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3.1 Simple Linear Regression 63 3 2
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3.1 Simple Linear Regression 65 con
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3.1 Simple Linear Regression 67 In
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Quantity Value Residual standard er
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3.2 Multiple Linear Regression 71 a
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3.2 Multiple Linear Regression 73 Y
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3.2 Multiple Linear Regression 75 T
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3.2 Multiple Linear Regression 77 w
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3.2 Multiple Linear Regression 79 i
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3.2 Multiple Linear Regression 81 S
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3.3 Other Considerations in the Reg
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x i = 3.3 Other Considerations in t
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3.4 The Marketing Plan 103 units wh
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y x 2 y x 2 3.5 Comparison of Linea
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3.5 Comparison of Linear Regression
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3.6 Lab: Linear Regression 109 p=1
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3.6 Lab: Linear Regression 111 Coef
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3.6 Lab: Linear Regression 113 > pl
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3.6 Lab: Linear Regression 115 > lm
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3.6 Lab: Linear Regression 117 > lm
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3.6 Lab: Linear Regression 119 Shel
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3.7 Exercises 121 (b) Answer (a) us
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3.7 Exercises 123 10. This question
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3.7 Exercises 125 (d) Create a scat
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4 Classification The linear regress
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4.2 Why Not Linear Regression? 129
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4.3 Logistic Regression 131 0 500 1
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4.3 Logistic Regression 133 β 1 do
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4.3 Logistic Regression 135 Coeffic
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4.3 Logistic Regression 137 Default
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4.4 Linear Discriminant Analysis 13
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4.5 A Comparison of Classification
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4.5 A Comparison of Classification
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4.6 Lab: Logistic Regression, LDA,
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4.7 Exercises 169 we will use obser
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Applied 4.7 Exercises 171 10. This
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4.7 Exercises 173 return(result) Th
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176 5. Resampling Methods 5.1 Cross
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178 5. Resampling Methods Mean Squa
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180 5. Resampling Methods LOOCV 10
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182 5. Resampling Methods Mean Squa
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184 5. Resampling Methods has highe
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186 5. Resampling Methods Error Rat
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188 5. Resampling Methods Y −2
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190 5. Resampling Methods Obs X Y Z
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192 5. Resampling Methods > mean((m
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194 5. Resampling Methods first is
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196 5. Resampling Methods Next, we
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198 5. Resampling Methods (f) When
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200 5. Resampling Methods predict.g
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6 Linear Model Selection and Regula
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6.1 Subset Selection 205 6.1 Subset
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6.1 Subset Selection 207 p = 20, th
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6.1 Subset Selection 209 #Variables
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6.1 Subset Selection 211 C p 10000
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6.1 Subset Selection 213 will lead
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6.2 Shrinkage Methods 215 obvious w
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6.2 Shrinkage Methods 217 regressio
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6.2 Shrinkage Methods 219 discussed
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6.2 Shrinkage Methods 221 respectiv
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6.2 Shrinkage Methods 223 Mean Squa
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6.2 Shrinkage Methods 225 To simpli
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6.2 Shrinkage Methods 227 (β j ) 0
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6.3 Dimension Reduction Methods 229
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9 Support Vector Machines In this c
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9.1 Maximal Margin Classifier 339 X
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9.1 Maximal Margin Classifier 341 h
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9.1 Maximal Margin Classifier 343 m
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9.2 Support Vector Classifiers 345
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9.2 Support Vector Classifiers 347
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9.3 Support Vector Machines 349 X2
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9.3 Support Vector Machines 351 in
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9.3 Support Vector Machines 353 X2
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9.4 SVMs with More than Two Classes
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9.5 Relationship to Logistic Regres
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9.6 Lab: Support Vector Machines 35
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9.6.3 ROC Curves 9.6 Lab: Support V
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Obs. X 1 X 2 Y 1 3 4 Red 2 2 2 Red
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9.7 Exercises 371 (b) Compute the c
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10 Unsupervised Learning Most of th
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10.2 Principal Components Analysis
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10.2.4 Other Uses for Principal Com
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10.3 Clustering Methods 387 K=2 K=3
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10.3 Clustering Methods 389 Data St
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10.3 Clustering Methods 391 X2 −2
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10.3 Clustering Methods 393 0.0 0.5
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Algorithm 10.2 Hierarchical Cluster
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10.3 Clustering Methods 397 Average
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10.3 Clustering Methods 399 0 2 4 6
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10.4 Lab 1: Principal Components An
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10.4 Lab 1: Principal Components An
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10.5 Lab 2: Clustering 405 Cluster
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10.6 Lab 3: NCI60 Data Example 407
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10.7 Exercises 413 differ: for inst
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10.7 Exercises 415 (b) At a certain
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10.7 Exercises 417 10. In this prob
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Index C p , 78, 205, 206, 210-213 R
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Index 421 Wage, 1, 2, 9, 10, 14, 26
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Index 423 noise, 22, 228 non-linear
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Index 425 read.table(), 48, 49 regs
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