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Interpreting and visualizing regression models using Stata:

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Bibliographische Detailangaben
Beteilige Person:	Mitchell, Michael N. (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	College Station, Tex. Stata Press 2012
Ausgabe:	1. ed.
Schriftenreihe:	A Stata Press publication
Schlagwörter:	Visualisierung Regressionsanalyse Stata Regressionsmodell
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024853249&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024853249&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
Beschreibung:	Literaturverz. S. 545 - 547
Umfang:	XXVIII, 558 S. graph. Darst.
ISBN:	9781597181075 1597181072

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Datensatz im Suchindex

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adam_text	Contents List of tables xv List of figures xvii Preface xxvii Acknowledgments xxix 1 Introduction 1 1.1 Overview of the book ........................... 1 1.2 Getting the most out of this book .................... 3 1.3 Downloading the example datasets and programs ........... 4 1.4 The GSS dataset ............................. 4 1.4.1 Income .............................. 5 1.4.2 Age ................................ 6 1.4.3 Education ............................ 10 1.4.4 Gender .............................. 12 1.5 The pain datasets ............................. 12 1.6 The optimism datasets .......................... 12 1.7 The school datasets ............................ 13 1.8 The sleep datasets ............................ 13 1 Continuous predictors 15 2 Continuous predictors: Linear 17 2.1 Chapter overview ............................. 17 2.2 Simple linear regression ......................... 17 2.2.1 Computing predicted means using the margins command . . 20 2.2.2 Graphing predicted means using the marginsplot command 22 2.3 Multiple regression ............................ 25 vi Contents 2.3.1 Computing adjusted means using the margins command . . 26 2.3.2 Some technical details about adjusted means ......... 28 2.3.3 Graphing adjusted means using the marginsplot command . 29 2.4 Checking for nonlinearity graphically .................. 30 2.4.1 Using scatterplots to check for nonlinearity .......... 31 2.4.2 Checking for nonlinearity using residuals ........... 31 2.4.3 Checking for nonlinearity using locally weighted smoother . 33 2.4.4 Graphing outcome mean at each level of predictor ...... 34 2.4.5 Summary ............................ 37 2.5 Checking for nonlinearity analytically ................. 37 2.5.1 Adding power terms ...................... 38 2.5.2 Using factor variables ...................... 39 2.6 Summary ................................. 43 3 Continuous predictors: Polynomials 45 3.1 Chapter overview ............................. 45 3.2 Quadratic (squared) terms ........................ 45 3.2.1 Overview ............................. 45 3.2.2 Examples ............................ 49 3.3 Cubic (third power) terms ........................ 55 3.3.1 Overview ............................. 55 3.3.2 Examples ............................ 56 3.4 Fractional polynomial regression .................... 62 3.4.1 Overview ............................. 62 3.4.2 Example using fractional polynomial regression ....... 66 3.5 Main effects with polynomial terms ................... 75 3.6 Summary ................................. 77 4 Continuous predictors: Piecewise models 79 4.1 Chapter overview ............................. 79 4.2 Introduction to piecewise regression models .............. 80 4.3 Piecewise with one known knot ..................... 82 Contents vii 4.3.1 Overview ............................. 82 4.3.2 Examples using the GSS .................... 83 4.4 Piecewise with two known knots .................... 91 4.4.1 Overview ............................. 91 4.4.2 Examples using the GSS .................... 91 4.5 Piecewise with one knot and one jump ................. 96 4.5.1 Overview ............................. 96 4.5.2 Examples using the GSS .................... 97 4.6 Piecewise with two knots and two jumps ................ 102 4.6.1 Overview ............................. 102 4.6.2 Examples using the GSS .................... 102 4.7 Piecewise with an unknown knot .................... 109 4.8 Piecewise model with multiple unknown knots ............. 113 4.9 Piecewise models and the marginsplot command ........... 120 4.10 Automating graphs of piecewise models ................ 123 4.11 Summary ................................. 126 5 Continuous by continuous interactions 127 5.1 Chapter overview ............................. 127 5.2 Linear by linear interactions ....................... 127 5.2.1 Overview ............................. 127 5.2.2 Example using GSS data .................... 132 5.2.3 Interpreting the interaction in terms of age .......... 133 5.2.4 Interpreting the interaction in terms of education ...... 135 5.2.5 Interpreting the interaction in terms of age slope ...... 137 5.2.6 Interpreting the interaction in terms of the educ slope . . . 138 5.3 Linear by quadratic interactions ..................... 140 5.3.1 Overview ............................. 140 5.3.2 Example using GSS data .................... 143 5.4 Summary ................................. 148 viii Contents 6 Continuous by continuous by continuous interactions 149 6.1 Chapter overview ............................. 149 6.2 Overview ................................. 149 6.3 Examples using the GSS data ...................... 154 6.3.1 A model without a three-way interaction ........... 154 6.3.2 A three-way interaction model ................. 158 6.4 Summary ................................. 164 II Categorical predictors 165 7 Categorical predictors 167 7.1 Chapter overview ............................. 167 7.2 Comparing two groups using a t test .................. 168 7.3 More groups and more predictors .................... 169 7.4 Overview of contrast operators ..................... 175 7.5 Compare each group against a reference group ............. 176 7.5.1 Selecting a specific contrast .................. 177 7.5.2 Selecting a different reference group .............. 178 7.5.3 Selecting a contrast and reference group ........... 179 7.6 Compare each group against the grand mean ............. 179 7.6.1 Selecting a specific contrast .................. 181 7.7 Compare adjacent means ......................... 182 7.7.1 Reverse adjacent contrasts ................... 185 7.7.2 Selecting a specific contrast .................. 186 7.8 Comparing the mean of subsequent or previous levels ......... 187 7.8.1 Comparing the mean of previous levels ............ 191 7.8.2 Selecting a specific contrast .................. 192 7.9 Polynomial contrasts ........................... 193 7.10 Custom contrasts ............................. 195 7.11 Weighted contrasts ............................ 198 7.12 Pairwise comparisons ...........................200 Contents ix 7.13 Interpreting confidence intervals ..................... 202 7.14 Testing categorical variables using regression ............. 205 7.15 Summary ................................. 208 8 Categorical by categorical interactions 209 8.1 Chapter overview ............................. 209 8.2 Two by two models: Example 1..................... 211 8.2.1 Simple effects .......................... 215 8.2.2 Estimating the size of the interaction ............. 216 8.2.3 More about interaction ..................... 217 8.2.4 Summary ............................ 218 8.3 Two by three models ........................... 218 8.3.1 Example 2............................ 218 8.3.2 Example 3............................ 223 8.3.3 Summary ............................ 228 8.4 Three by three models: Example 4................... 228 8.4.1 Simple effects .......................... 230 8.4.2 Simple contrasts ......................... 231 8.4.3 Partial interaction ....................... 233 8.4.4 Interaction contrasts ...................... 234 8.4.5 Summary ............................ 236 8.5 Unbalanced designs ............................ 236 8.6 Main effects with interactions: anova versus regress .......... 241 8.7 Interpreting confidence intervals ..................... 244 8.8 Summary ................................. 246 9 Categorical by categorical by categorical interactions 249 9.1 Chapter overview ............................. 249 9.2 Two by two by two models ....................... 250 9.2.1 Simple interactions by season ................. 252 9.2.2 Simple interactions by depression status ........... 253 9.2.3 Simple effects .......................... 255 χ Contents 9.3 Two by two by three models ....................... 255 9.3.1 Simple interactions by depression status ........... 258 9.3.2 Simple partial interaction by depression status ........ 258 9.3.3 Simple contrasts ......................... 260 9.3.4 Partial interactions ....................... 260 9.4 Three by three by three models and beyond .............. 262 9.4.1 Partial interactions and interaction contrasts ......... 264 9.4.2 Simple interactions ....................... 268 9.4.3 Simple effects and simple comparisons ............ 271 9.5 Summary ................................. 272 III Continuous and categorical predictors 273 10 Linear by categorical interactions 275 10.1 Chapter overview ............................. 275 10.2 Linear and two-level categorical: No interaction ............ 275 10.2.1 Overview ............................. 275 10.2.2 Examples using the GSS .................... 278 10.3 Linear by two-level categorical interactions ............... 283 10.3.1 Overview ............................. 283 10.3.2 Examples using the GSS .................... 285 10.4 Linear by three-level categorical interactions .............. 290 10.4.1 Overview ............................. 290 10.4.2 Examples using the GSS .................... 293 10.5 Summary ................................. 299 11 Polynomial by categorical interactions 301 11.1 Chapter overview ............................. 301 11.2 Quadratic by categorical interactions .................. 301 11.2.1 Overview ............................. 302 11.2.2 Quadratic by two-level categorical ............... 305 11.2.3 Quadratic by three-level categorical .............. 312 Contents xi 11.3 Cubic by categorical interactions .................... 318 11.4 Summary ................................. 323 12 Piecewise by categorical interactions 325 12.1 Chapter overview ............................. 325 12.2 One knot arid one jump ......................... 328 12.2.1 Comparing slopes across gender ................ 332 12.2.2 Comparing slopes across education .............. 333 12.2.3 Difference in differences of slopes ............... 333 12.2.4 Comparing changes in intercepts ............... 334 12.2.5 Computing and comparing adjusted means .......... 334 12.2.6 Graphing adjusted means ................... 337 12.3 Two knots and two jumps ........................ 341 12.3.1 Comparing slopes across gender ................ 346 12.3.2 Comparing slopes across education .............. 347 12.3.3 Difference in differences of slopes ............... 348 12.3.4 Comparing changes in intercepts by gender ......... 349 12.3.5 Comparing changes in intercepts by education ........ 350 12.3.6 Computing and comparing adjusted means .......... 351 12.3.7 Graphing adjusted means ................... 354 12.4 Comparing coding schemes ....................... 356 12.4.1 Coding scheme #1....................... 356 12.4.2 Coding scheme #2....................... 358 12.4.3 Coding scheme #3....................... 360 12.4.4 Coding scheme #4....................... 361 12.4.5 Choosing coding schemes .................... 363 12.5 Summary ................................. 364 13 Continuous by continuous by categorical interactions 365 13.1 Chapter overview ............................. 365 13.2 Linear by linear by categorical interactions ............... 366 13.2.1 Fitting separate models for males and females ........ 366 xjj Contents 13.2.2 Fitting a combined model for males and females ....... 368 13.2.3 Interpreting the interaction focusing in the age slope .... 370 13.2.4 Interpreting the interaction focusing on the educ slope . . . 372 13.2.5 Estimating and comparing adjusted means by gender .... 374 13.3 Linear by quadratic by categorical interactions ............ 376 13.3.1 Fitting separate models for males and females ........ 376 13.3.2 Fitting a common model for males and females ....... 378 13.3.3 Interpreting the interaction .................. 379 13.3.4 Estimating and comparing adjusted means by gender .... 380 13.4 Summary ................................. 382 14 Continuous by categorical by categorical interactions 383 14.1 Chapter overview ............................. 383 14.2 Simple effects of gender on the age slope ................ 387 14.3 Simple effects of education on the age slope .............. 388 14.4 Simple contrasts on education for the age slope ............ 389 14.5 Partial interaction on education for the age slope ........... 389 14.6 Summary ................................. 390 IV Beyond ordinary linear regression 391 15 Multilevel models 393 15.1 Chapter overview ............................. 393 15.2 Example 1: Continuous by continuous interaction ........... 394 15.3 Example 2: Continuous by categorical interaction ........... 397 15.4 Example 3: Categorical by continuous interaction ........... 401 15.5 Example 4: Categorical by categorical interaction ........... 404 15.6 Summary ................................. 408 16 Time as a continuous predictor 411 16.1 Chapter overview ............................. 411 16.2 Example 1: Linear effect of time .................... 412 16.3 Example 2: Linear effect of time by a categorical predictor ...... 416 Contents xiii 16.4 Example 3: Piecewise modeling of time ................. 421 16.5 Example 4: Piecewise effects of time by a categorical predictor . . . 426 16.5.1 Baseline slopes ......................... 430 16.5.2 Change in slopes: Treatment versus baseline ......... 431 16.5.3 Jump at treatment ....................... 432 16.5.4 Comparisons among groups .................. 433 16.6 Summary ................................. 434 17 Time as a categorical predictor 437 17.1 Chapter overview ............................. 437 17.2 Example 1: Time treated as a categorical variable ........... 438 17.3 Example 2: Time (categorical) by two groups ............. 443 17.4 Example 3: Time (categorical) by three groups ............ 447 17.5 Comparing models with different residual covariance structures . . . 452 17.6 Summary ................................. 454 18 Nonlinear models 455 18.1 Chapter overview ............................. 455 18.2 Binary logistic regression ......................... 456 18.2.1 A logistic model with one categorical predictor ....... 456 18.2.2 A logistic model with one continuous predictor ....... 463 18.2.3 A logistic model with covariates ................ 465 18.3 Multinomial logistic regression ..................... 470 18.4 Ordinal logistic regression ........................ 475 18.5 Poisson regression ............................. 478 18.6 More applications of nonlinear models ................. 481 18.6.1 Categorical by categorical interaction ............. 481 18.6.2 Categorical by continuous interaction ............. 487 18.6.3 Piecewise modeling ....................... 492 18.7 Summary ................................. 498 19 Complex survey data 499 XIV Contents V Appendices 505 A The margins command 507 A.I The predict() and expression() options ................. 507 A. 2 The at() option .............................. 510 A.3 Margins with factor variables ...................... 513 A.4 Margins with factor variables and the at() option ........... 517 A. 5 The dydxQ and related options ..................... 519 В The marginsplot command 523 С The contrast command 535 D The pwcompare command 539 References 545 Author index 549 Subject index 551 Interpreting and Visualizing Regression Models Using Stata provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. Including over 180 figures, the book illustrates linear models with continuous predictors (modeled linearly, using polynomials, and piecewise), interactions of continuous predictors, categorical predictors, interactions of categorical predictors, as well as interactions of continuous and categorical predictors. The book also illustrates how to interpret (and visualize) results from multilevel models, models where time is a continuous predictor, models with time as a categorical predictor, nonlinear models (such as logistic or ordinal logistic regression), as well as models involving complex survey data. The examples illustrate the use of the Stata margins, marginsplot, contrast, and pwcompare commands. For example, the use of the contrast command is illustrated for models with interactions of categorical variables, showing how you can precisely understand and dissect such interactions with surgical precision. If you ever find yourself wishing for simple and straightforward advice about how to interpret and visualize regression models using Stata, this book is for you. Michael Mitchell is a senior statistician in disaster preparedness and response. He is the author of Л Visual Guide to Stata Graphics as well as Data Management Using Stata. Previously, he worked for 12 years as a statistical consultant and manager of the UCLA ATS Statistical Consulting Group. There, he envisioned the UCLA Statistical Consulting Resources website and wrote hundreds of web pages about Stata. Telephone: 979-696-4600 800-782-8272 бОО-ЅТАТАРС Fax: 979-696-4601 service@stata-press.com stata-press.com ISBN 978-1-59718-107-5 90000 > 9 781597И 181075
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owner_facet	DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-N2 DE-703 DE-945 DE-83 DE-739 DE-11 DE-188 DE-522 DE-91 DE-BY-TUM DE-20 DE-824 DE-384 DE-355 DE-BY-UBR
physical	XXVIII, 558 S. graph. Darst.
publishDate	2012
publishDateSearch	2012
publishDateSort	2012
publisher	Stata Press
record_format	marc
series2	A Stata Press publication
spellingShingle	Mitchell, Michael N. Interpreting and visualizing regression models using Stata Visualisierung (DE-588)4188417-6 gnd Regressionsanalyse (DE-588)4129903-6 gnd Stata (DE-588)4617285-3 gnd Regressionsmodell (DE-588)4127980-3 gnd
subject_GND	(DE-588)4188417-6 (DE-588)4129903-6 (DE-588)4617285-3 (DE-588)4127980-3
title	Interpreting and visualizing regression models using Stata
title_auth	Interpreting and visualizing regression models using Stata
title_exact_search	Interpreting and visualizing regression models using Stata
title_full	Interpreting and visualizing regression models using Stata Michael N. Mitchell
title_fullStr	Interpreting and visualizing regression models using Stata Michael N. Mitchell
title_full_unstemmed	Interpreting and visualizing regression models using Stata Michael N. Mitchell
title_short	Interpreting and visualizing regression models using Stata
title_sort	interpreting and visualizing regression models using stata
topic	Visualisierung (DE-588)4188417-6 gnd Regressionsanalyse (DE-588)4129903-6 gnd Stata (DE-588)4617285-3 gnd Regressionsmodell (DE-588)4127980-3 gnd
topic_facet	Visualisierung Regressionsanalyse Stata Regressionsmodell
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024853249&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024853249&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT mitchellmichaeln interpretingandvisualizingregressionmodelsusingstata

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