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Produktbild: Multilevel Modeling Using R

Multilevel Modeling Using R

79,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.05.2019

Abbildungen

2 tables

Verlag

KNV Besorgung

Seitenzahl

242

Maße (L/B/H)

23,1/15,6/1,7 cm

Gewicht

381 g

Auflage

2nd edition

Sprache

Englisch

ISBN

978-1-138-48067-4

Beschreibung

Rezension

"This book is the second edition of a hugely popular title on multilevel modelling (MLM) using R software. Assuming a basic understanding of how a linear regression model works, if someone is looking for a complete reference on how to fit multilevel models with R, then look no further. Even for those not accustomed to the mathematical details of regression modelling, the provided overview with practical examples and R code should get one up to speed. This book is concise, to the point, and a hands-on, how-to reference on multilevel modelling. Through their clear writing style, the authors have provided answers to all of the essential questions a practitioner might have in fitting a multilevel model. In essence, the book presents straightforward explanations of basic MLM, multilevel generalized linear models, Bayesian multilevel modelling, multivariate multilevel modelling, and how to fit them using R."- Enayet Raheem, ISCB News, July 2020

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.05.2019

Abbildungen

2 tables

Verlag

KNV Besorgung

Seitenzahl

242

Maße (L/B/H)

23,1/15,6/1,7 cm

Gewicht

381 g

Auflage

2nd edition

Sprache

Englisch

ISBN

978-1-138-48067-4

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Multilevel Modeling Using R
  • 1: Linear Models
    Simple Linear Regression
    Estimating Regression Models with Ordinary Least Squares
    Distributional Assumptions Underlying Regression
    Coefficient of Determination
    Inference for Regression Parameters
    Multiple Regression
    Example of Simple Linear Regression by Hand
    Regression in R
    Interaction Terms in Regression
    Categorical Independent Variables
    Checking Regression Assumptions with R
    Summary

    2: An Introduction to Multilevel Data Structure
    Nested Data and Cluster Sampling Designs
    Intraclass Correlation
    Pitfalls of Ignoring Multilevel Data Structure
    Multilevel Linear Models
    Random Intercept
    Random Slopes
    Centering
    Basics of Parameter Estimation with MLMs
    Maximum Likelihood Estimation
    Restricted Maximum Likelihood Estimation
    Assumptions Underlying MLMs
    Overview of 2 level MLMs
    Overview of 3 level MLMs
    Overview of longitudinal designs and their relationships to MLMs
    Summary

    3: Fitting 2-level Models
    Simple (Intercept only) Multilevel Models
    Interactions and Cross Level Interactions using R
    Random Coefficients Models using R
    Centering Predictors
    Additional Options
    Parameter Estimation Method
    Estimation Controls
    Comparing Model fit
    Lme4 and hypothesis testing
    Summary

    4: 3 Level and Higher Models
    Defining simple 3-level Models using the lme4 package
    Defining simple models with more than three levels in the lme4 package Random Coefficients models with Three or More Levels in the lme4
    Package
    Summary

    5: Longitudinal Data Analysis using Multilevel Models
    The Multilevel Longitudinal Framework
    Person Period Data Structure
    Fitting Longitudinal Models using the lme4 package
    Changing the Covariance Structure of Longitudinal Models
    Benefits of Multilevel Modeling for Longitudinal Analysis
    Summary

    6: Graphing Data in Multilevel Contexts
    Plots for Linear Models
    Plotting Nested Data
    Using the Lattice Package
    Plotting Model Results using the Effects Package
    Summary

    7: Brief Introduction to Generalized Linear Models
    Logistic Regression Model for a Dichotomous Outcome Variable
    Logistic Regression Model for an Ordinal Outcome Variable
    Multinomial Logistic Regression
    Models for Count Data
    Poisson Regression
    Models for Overdispersed Count data
    Summary

    8: Multilevel Generalized Linear Models (MGLM)
    MGLMs for a Dichotomous Outcome Variable
    Random Intercept Logistic Regression
    Random Coefficient Logistic Regression
    Inclusion of Additional level 1 and level 2 effects in MGLM
    MLGM for an Ordinal Outcome Variable
    Random Intercept Logistic Regression
    MGLM for Count Data
    Random Intercept Poisson Regression
    Random Coefficient Poisson Regression
    Inclusion of additional level-2 effects to the multilevel Poisson regression
    model
    Summary

    9: Bayesian Multilevel Modeling
    MCMCglmm For a Normally Distributed Response Variable
    Including level-2 Predictors with MCMCglmm
    User Defined Priors
    MCMCglmm For a Dichotomous Dependent Variable
    MCMCglmm for a Count Dependent Variable
    Summary

    10: Advanced Issues in Multilevel Modeling
    Robust statistics in the multilevel context
    Identifying potential outliers in single level data
    Identifying potential outliers in multilevel data
    Identifying potential multilevel outliers using R
    Robust and Rank Based Estimation for multilevel models
    Fitting Robust and Rank Based Multilevel Models in R
    Multilevel Lasso
    Fitting the Multilevel Lasso in R
    Multivariate Multilevel Models
    Multilevel Generalized Additive Models
    Fitting GAMM using R
    Predicting Level-2 Outcomes with Level-1 Variables
    Power Analysis for Multilevel Models
    Summary

    Appendix: An Introduction to R
    Running Statistical Analyses in R
    Reading Data into R
    Missing Data
    Types of Data
    Additional R Environment Options