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224 7. INTERACTIONSlog GDP year 20006 7 8 9African nationslog GDP year 20007 8 9 10 11Non-African nations0 1 2 3 4 5 6Terrain Ruggedness Index0 1 2 3 4 5Terrain Ruggedness IndexFIGURE 7.4. Posterior predictions for the terrain ruggedness model, includingthe interaction between Africa and ruggedness.mu
7.1. BUILDING AN INTERACTION 225Plotting these calculations is just like previous examples. Here’s the code:# plot African nations with regressiond.A1
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Statistical RethinkingA BAYESIAN CO
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4 CONTENTS5.5. Ordinary least squar
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PrefaceMasons, when they start upon
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HOW TO USE THIS BOOK 9least a minor
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HOW TO USE THIS BOOK 11than initial
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1 e Golem of PragueIn the 16th cent
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16 1. THE GOLEM OF PRAGUEconverses
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1.2. WRECKING PRAGUE 19model P 1B ,
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1.2. WRECKING PRAGUE 21e dominant r
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1.3. THREE TOOLS FOR GOLEM ENGINEER
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2 Small Worlds and Large WorldsWhen
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2.1. PROBABILITY IS JUST COUNTING 3
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2.2. COLOMBO’S FIRST BAYESIAN MOD
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W L W W W L W L W2.2. COLOMBO’S F
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2.3. COMPONENTS OF THE MODEL 41not
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2.3. COMPONENTS OF THE MODEL 43You
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2.3. COMPONENTS OF THE MODEL 45e ma
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2.4. MAKING THE MODEL GO 47other pr
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2.4. MAKING THE MODEL GO 495 points
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2.4. MAKING THE MODEL GO 51just the
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2.4. MAKING THE MODEL GO 53between
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2.6. PRACTICE 55Medium.2.6.5. m1. R
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2.6. PRACTICE 57implied by the equa
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3 Sampling the ImaginaryLots of boo
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3. SAMPLING THE IMAGINARY 61a probl
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3.2. SAMPLING TO SUMMARIZE 63plot(
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3.2. SAMPLING TO SUMMARIZE 65Densit
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3.2. SAMPLING TO SUMMARIZE 67In con
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3.2. SAMPLING TO SUMMARIZE 69Densit
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3.3. SAMPLING TO SIMULATE PREDICTIO
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3.5. PRACTICE 793.5. PracticeEasy.
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3.5. PRACTICE 813.5.17. Predict sec
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84 4. LINEAR MODELSFIGURE 4.1. e Pt
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86 4. LINEAR MODELSexperiment with
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88 4. LINEAR MODELS4.1.4.2. Epistem
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90 4. LINEAR MODELSe approach above
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92 4. LINEAR MODELS'data.frame': 54
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94 4. LINEAR MODELSe point isn’t
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96 4. LINEAR MODELShave the samples
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98 4. LINEAR MODELSere’s no troub
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100 4. LINEAR MODELS)Note the comma
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102 4. LINEAR MODELSpreviously obse
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104 4. LINEAR MODELS4.4. Adding a p
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106 4. LINEAR MODELS(1) What is the
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108 4. LINEAR MODELSdata(Howell1)d
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110 4. LINEAR MODELSRethinking: Wha
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112 4. LINEAR MODELSheight140 150 1
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116 4. LINEAR MODELSYou end up with
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118 4. LINEAR MODELS(1) Use link to
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122 4. LINEAR MODELSRethinking: Lin
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126 4. LINEAR MODELS4.7.1. e1. In t
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5 Multivariate Linear ModelsOne of
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5.1. SPURIOUS ASSOCIATION 131Divorc
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5.1. SPURIOUS ASSOCIATION 133But me
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5.1. SPURIOUS ASSOCIATION 135abmbas
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5.1. SPURIOUS ASSOCIATION 137this i
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5.1. SPURIOUS ASSOCIATION 139slower
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5.1. SPURIOUS ASSOCIATION 141Median
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5.1. SPURIOUS ASSOCIATION 143(a)(b)
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5.2. MASKED RELATIONSHIP 145N
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5.2. MASKED RELATIONSHIP 147a ~ dno
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5.2. MASKED RELATIONSHIP 149dcc$log
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5.3. WHEN ADDING VARIABLES HURTS 15
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5.4. CATEGORICAL VARIABLES 161$ wei
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5.4. CATEGORICAL VARIABLES 1635.4.2
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5.4. CATEGORICAL VARIABLES 165# sam
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168 5. MULTIVARIATE LINEAR MODELS5.
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170 5. MULTIVARIATE LINEAR MODELS5.
Page 173 and 174: 6 Model Selection, Comparison, and
Page 175 and 176: 6.1. THE PROBLEM WITH PARAMETERS 17
Page 183 and 184: 6.2. INFORMATION THEORY AND MODEL P
Page 189 and 190: 6.3. AKAIKE INFORMATION CRITERION 1
Page 197 and 198: 6.4. DEVIANCE INFORMATION CRITERION
Page 203 and 204: 6.5. USING AIC 203helps guard again
Page 205 and 206: 6.5. USING AIC 205compare( m6.11 ,
Page 207 and 208: 6.5. USING AIC 207e attitude this b
Page 209 and 210: 6.5. USING AIC 209kcal.per.g0.5 0.7
Page 211 and 212: 6.7. PRACTICE 211Consider by analog
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Page 216 and 217: 216 7. INTERACTIONSFIGURE 7.1. TOP:
Page 218 and 219: 218 7. INTERACTIONSlog(rgdppc_2000)
Page 220 and 221: 220 7. INTERACTIONSird, we may want
Page 222 and 223: 222 7. INTERACTIONSlog GDP year 200
Page 226 and 227: 226 7. INTERACTIONSInteraction mode
Page 228 and 229: 228 7. INTERACTIONSthis model and t
Page 230 and 231: 230 7. INTERACTIONSlog GDP year 200
Page 232 and 233: 232 7. INTERACTIONSe main effect li
Page 234 and 235: 234 7. INTERACTIONSbs -38.91 34.94s
Page 236 and 237: 236 7. INTERACTIONSe primary reason
Page 238 and 239: 238 7. INTERACTIONSNow for the plot
Page 240 and 241: 240 7. INTERACTIONS7.4. Higher-orde
Page 243 and 244: 8 Markov Chain Monte Carlo Estimati
Page 245 and 246: 8.1. GOOD KING MARKOV AND HIS ISLAN
Page 247 and 248: 8.2. MARKOV CHAIN MONTE CARLO 247fo
Page 249 and 250: 8.2. MARKOV CHAIN MONTE CARLO 249No
Page 251 and 252: 8.3. EASY HMC: MAP2STAN 251We’re
Page 253 and 254: 8.3. EASY HMC: MAP2STAN 253$ cont_a
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Page 257 and 258: 8.3. EASY HMC: MAP2STAN 257FIGURE 8
Page 259 and 260: 8.4. CARE AND FEEDING OF YOUR MARKO
Page 265: 8.6. PRACTICE 265And since the chai
Page 268 and 269: 268 9. BIG ENTROPY AND THE GENERALI
Page 270 and 271: 270 9. BIG ENTROPY AND THE GENERALI
Page 273 and 274: 10 Distance and DurationA curious t
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10.2. GAMMA 275can be quite complic
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278 11. COUNTING AND CLASSIFICATION
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304 12. MONSTERS AND MIXTUREShere?
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306 12. MONSTERS AND MIXTURESdensit
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308 12. MONSTERS AND MIXTURESfootbr
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310 12. MONSTERS AND MIXTURESWhy di
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312 12. MONSTERS AND MIXTURESm11.3
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314 12. MONSTERS AND MIXTURESResult
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316 12. MONSTERS AND MIXTURESanswer
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318 12. MONSTERS AND MIXTURESDensit
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320 12. MONSTERS AND MIXTURESBut we
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322 12. MONSTERS AND MIXTURESdisper
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324 12. MONSTERS AND MIXTURESe resu
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326 12. MONSTERS AND MIXTURESDensit
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328 12. MONSTERS AND MIXTURESR code
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330 12. MONSTERS AND MIXTURESPoisso
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332 12. MONSTERS AND MIXTURESthe ga
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334 12. MONSTERS AND MIXTURESdata=d
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336 12. MONSTERS AND MIXTURESma2 ~
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338 13. MULTILEVEL MODELSrepeat obs
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340 13. MULTILEVEL MODELSSo how do
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342 13. MULTILEVEL MODELSprobabilit
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344 13. MULTILEVEL MODELSa conseque
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346 13. MULTILEVEL MODELSCompute th
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348 13. MULTILEVEL MODELSabsolute e
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350 13. MULTILEVEL MODELS)sigma_act
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14 Multilevel Models II: Slopes14.1
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14.1. EVERYTHING CAN VARY AND PROBA
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14.1. EVERYTHING CAN VARY AND PROBA
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360 15. MISSING DATA AND OTHER OPPO
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16 Writing Statistics379
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382 ENDNOTES12. For an autopsy of t
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384 ENDNOTES45. Fisher (1925), in C
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386 ENDNOTES77. See two famous edit
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388 ENDNOTES113. Hurlbert (1984) is
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390 BibliographyFrank, S. A. (2011)
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392 BibliographyProulx, S. R. and A
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IndexAkaike (1973), 380, 383Akaike
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INDEX 397non-identifiability, 153Oc
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