Multilevel modeling using R:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton
CRC Press
[2019]
|
| Ausgabe: | Second edition |
| Schriftenreihe: | Statistics in the social and behavioral sciences series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis Klappentext |
| Beschreibung: | ix, 242 Seiten Illustrationen, Diagramme |
| ISBN: | 9781138480674 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804180047881306112 |
|---|---|
| adam_text | Contents Authors............................................................................................................................ίχ 1 Linear Models......................................................................................... 1 Simple Linear Regression........................................................................2 Estimating Regression Models with Ordinary Least Squares................ 2 Distributional Assumptions Underlying Regression..............................3 Coefficient of Determination................................................................... 4 Inference for Regression Parameters...................................................... 5 Multiple Regression................................................................................. 7 Example of Simple Linear Regression by Hand...................................... 9 Regression in R.......................................................................................11 Interaction Terms in Regression............................................................ 14 Categorical Independent Variables........................................................15 Checking Regression Assumptions with R........................................... 18 Summary................................................................................................21 2 An Introduction to Multilevel Data Structure................................... 23 Nested Data and Cluster Sampling Designs.........................................23 Intraclass
Correlation............................................................................ 24 Pitfalls of Ignoring Multilevel Data Structure....................................... 29 Multilevel Linear Models...................................................................... 29 Random Intercept.............................................................................. 30 Random Slopes.................................................................................. 31 Centering...........................................................................................34 Basics of Parameter Estimation with MLMs..................................... 35 Maximum Likelihood Estimation................................................ 36 Restricted Maximum Likelihood Estimation...............................36 Assumptions Underlying MLMs.......................................................37 Overview of Two-Level MLMs......................................................... 37 Overview of Three-Level MLMs...................................................... 39 Overview of Longitudinal Designs and Their Relationship to MLMs................................................................................................ 40 Summary................................................................................................41 3 Fitting Two-Level Models in R............................................................43 Simple (Intercept-Only) Multilevel Models...........................................43 Interactions and Cross-Level Interactions Using
R...............................48 Random Coefficients Models using R....................................................50 Centering Predictors.............................................................................. 55
vi Contents Additional Options.................................................................................... 56 Parameter Estimation Method............................................................. 56 Estimation Controls...............................................................................56 Comparing Model Fit............................................................................57 lme4 and Hypothesis Testing............................................................... 58 Summary............................................. 61 Note..............................................................................................................61 4 Three-Level and Higher Models............................................................ 63 Defining Simple Three-Level Models Using the lme4 Package............. 63 Defining Simple Models with More than Three Levels in the lme4 Package........................................................................................................70 Random Coefficients Models with Three or More Levels in the lme4 Package.............................................................................................. 71 Summary..................................................................................................... 74 Note..............................................................................................................74 5 Longitudinal Data Analysis Using Multilevel Models....................... 75 The Multilevel Longitudinal Framework.................................................75 Person
Period Data Structure.................................................................... 77 Fitting Longitudinal Models Using the lme4 Package............................78 Benefits of Using Multilevel Modeling for Longitudinal Analysis.......82 Summary.....................................................................................................83 Note............................................................................................................. 83 6 Graphing Data in Multilevel Contexts...................................................85 Plots for Linear Models..............................................................................90 Plotting Nested Data..................................................................................93 Using the Lattice Package..........................................................................94 Plotting Model Results Using the Effects Package................................104 Summary................................................................................................... 114 7 Brief Introduction to Generalized Linear Models.............................. 115 Logistic Regression Model for a Dichotomous Outcome Variable......116 Logistic Regression Model for an Ordinal Outcome Variable............. 120 Multinomial Logistic Regression............................................................123 Models for Count Data.............................................................................126 Poisson Regression...............................................................................126
Models for Overdispersed Count Data.............................................. 128 Summary................................................................................................... 131 8 Multilevel Generalized Linear Models (MGLMs)............................. 133 MGLMs for a Dichotomous Outcome Variable......................................133 Random Intercept Logistic Regression.............................................. 134 Random Coefficients Logistic Regression..........................................137 Inclusion of Additional Level-1 and Level-2 Effects in MGLM.......139
Contents Vil MGLM for an Ordinal Outcome Variable.............................................. 143 Random Intercept Logistic Regression.............................................. 143 MGLM for Count Data............................................................................. 146 Random Intercept Poisson Regression............................................... 147 Random Coefficient Poisson Regression............................................148 Inclusion of Additional Level-2 Effects to the Multilevel Poisson Regression Model.................................................................................150 Summary................................................................................................... 158 9 Bayesian Multilevel Modeling..............................................................159 MCMCglmm for a Normally Distributed Response Variable..............162 Including Level-2 Predictors with MCMCglmm...................................168 User Defined Priors.................................................................................. 174 MCMCglmm for a Dichotomous Dependent Variable.......................... 178 MCMCglmm for a Count-Dependent Variable......................................181 Summary................................................................................................... 187 10 Advanced Issues in Multilevel Modeling............................................189 Robust Statistics in the Multilevel Context............................................ 189 Identifying Potential Outliers in Single-Level
Data..............................190 Identifying Potential Outliers in Multilevel Data..................................192 Identifying Potential Multilevel Outliers Using R.................................194 Robust and Rank-Based Estimation for Multilevel Models................. 202 Fitting Robust and Rank-Based Multilevel Models in R....................... 205 Cauchy..................................................................................................208 Slash......................................................................................................208 Contaminated...................................................................................... 209 Multilevel Lasso........................................................................................210 Fitting the Multilevel Lasso in R.............................................................211 Multivariate Multilevel Models.............................................................. 215 Multilevel Generalized Additive Models.......................................... 217 Fitting GAMM using R............................................................................ 218 Predicting Level-2 Outcomes with Level-1 Variables............................223 Power Analysis for Multilevel Models...................................................227 Summary................................................................................................... 231 References.......................................................................................................233
Index.................................................................................................................247
I ікс its bestsellinq predecessor. Multilevel Modeling Using R, Second Edition provides lhe reader vatti a helpful guide to conducting multilevel data modeling using the R software environment. Attor reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. New in the Second Edition • • • • • Features the use of Imer (instead of Ime) and including the most up to date approaches for obtaining confidence intervals for the model parameters. Discusses measures of R2 (the squared multiple correlation coefficient) and overall model fit. Adds a chapter on nonparametric and robust approaches to estimating multilevel models, including rank based, heavy tailed distributions, and the multilevel lasso. Includes a new chapter on multivariate multilevel models. Presents new sections on micro-macro models and multilevel generalized additive models. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research.
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| illustrated | Illustrated |
| indexdate | 2024-07-10T08:29:33Z |
| institution | BVB |
| isbn | 9781138480674 |
| language | English |
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| physical | ix, 242 Seiten Illustrationen, Diagramme |
| publishDate | 2019 |
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| series2 | Statistics in the social and behavioral sciences series |
| spelling | Finch, W. Holmes Verfasser (DE-588)1058559753 aut Multilevel modeling using R W. Holmes Finch, Jocelyn E. Bolin, and Ken Kelley Second edition Boca Raton CRC Press [2019] ix, 242 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Statistics in the social and behavioral sciences series R Programm (DE-588)4705956-4 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Sozialwissenschaften (DE-588)4055916-6 s Statistik (DE-588)4056995-0 s R Programm (DE-588)4705956-4 s DE-604 Multivariate Analyse (DE-588)4040708-1 s Bolin, Jocelyn E. Verfasser (DE-588)105855980X aut Kelley, Ken Verfasser (DE-588)1058559850 aut V:DE-605 application/pdf http://digitale-objekte.hbz-nrw.de/storage2/2019/06/18/file_11/8482183.pdf Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031274192&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031274192&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Finch, W. Holmes Bolin, Jocelyn E. Kelley, Ken Multilevel modeling using R R Programm (DE-588)4705956-4 gnd Multivariate Analyse (DE-588)4040708-1 gnd Statistik (DE-588)4056995-0 gnd Sozialwissenschaften (DE-588)4055916-6 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4040708-1 (DE-588)4056995-0 (DE-588)4055916-6 |
| title | Multilevel modeling using R |
| title_auth | Multilevel modeling using R |
| title_exact_search | Multilevel modeling using R |
| title_full | Multilevel modeling using R W. Holmes Finch, Jocelyn E. Bolin, and Ken Kelley |
| title_fullStr | Multilevel modeling using R W. Holmes Finch, Jocelyn E. Bolin, and Ken Kelley |
| title_full_unstemmed | Multilevel modeling using R W. Holmes Finch, Jocelyn E. Bolin, and Ken Kelley |
| title_short | Multilevel modeling using R |
| title_sort | multilevel modeling using r |
| topic | R Programm (DE-588)4705956-4 gnd Multivariate Analyse (DE-588)4040708-1 gnd Statistik (DE-588)4056995-0 gnd Sozialwissenschaften (DE-588)4055916-6 gnd |
| topic_facet | R Programm Multivariate Analyse Statistik Sozialwissenschaften |
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