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Long J Longitudinal Data Analysis For The Behavioral Sciences Using R 2011

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Category:	eBooks
Infohash	5d77f5cf5bbdc67fe05da70a5a13fdb2487cbc1b
Size:	36.2 MB in 1 file
Release Date(s):	Torrent Release: 10-02-2024 by user
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DESCRIPTION

Textbook in PDF format

Preface
Introduction
Statistical Computing
Preliminary Issues
Means Versus Correlations
Measurement Issues
Response Variable Assumptions
Conceptual Overview of Linear Mixed Effects Regression
Goals of Inference
Random Effects
How Important Are Random Effects
Traditional Approaches
MPLS Data Set
Statistical Strategy
LMER and Multimodel Inference
Statistical Hypotheses
Overview of the Remainder of the Book
Brief Introduction to R
Obtaining and Installing R
Functions and Packages
Essential Syntax
Prompt Versus Script Files
Input and Output Appearance in This Book
Quitting R
Terminating a Process
Basic Calculations
Objects
Concatenation
Statistical Functions
Data Types
Missing Values
Matrices, Data Frames, and Lists
Vector
Matrix
Data Frame
List
Indexing
Matrix and Data Frame
Vector
List
Sorting
Recoding
Saving Objects
Loading and Listing Objects
User-Defined Functions
Repetitive Operations
rdply
for Loop
Linear Regression
Getting Help
Summary of Functions
Data Structures and Longitudinal Analysis
Longitudinal Data Structures
Wide Format
Long Format
Reading an External File
Reading a Text File With readtable
Displaying the Data Frame
Converting and Recoding Variables
Basic Statistics for Wide-Format Data
Means, Variances, and Correlations
Missing Data Statistics
Conditioning on Static Predictors
Reshaping Data
Wide to Long Format
Long to Wide Format
Basic Statistics for Long-Format Data
Means, Variances, and Correlations
Missing Data Statistics
Conditioning on Static Predictors
Data Structures and Balance on Time
Missing Data in LMER Analysis
Retain or Omit Missing Data Rows
Missing Data Concepts
Missing Completely at Random
Missing at Random
Not Missing at Random
Missing Data Mechanisms and Statistical Analysis
Missing Data Simulation
LMER Analysis
Extensions to More Complex Data Structures
Multiple Dynamic Variables
Unbalanced Data
Graphing Longitudinal Data
Graphing and Statistical Strategy
Graphing With ggplot
Graph Components
Layering
Graphing Individual-Level Curves
Superimposed Individual Curves
Facet Plots of Individual Curves
Selecting Subsets
Graphing Fitted Curves
Graphing Group-Level Curves
Curve of the Means
Graphing Fitted Curves
Graphing Individual-Level and Group-Level Curves
Conditioning on Static Predictors
Categorical Static Predictors
Quantitative Static Predictors
Customizing Graphs
Customizing Axes
Customizing Facets
Customizing the Legend
Summary of ggplot Components
Introduction to Linear Mixed Effects Regression
Traditional Regression and the Linear Model
Regression Examples
Single Quantitative Predictor
Analysis of Covariance
Interaction Model
Linear Mixed Effects Regression
LMER as a Multilevel Model
Random Effects as Errors
Assumptions Regarding Random Effects and Random Error
Random Effects and Correlated Observations
Estimating the LMER Model
Time as a Predictor
Anchoring the Intercept
LMER With Static Predictors
Intercept Effects
Slope and Intercept Effects
Initial Status as a Static Predictor
Extensions to More Complex Models
Summary of lmer Syntax
Additional Details of LMER
General Form of the LMER Model
Variance-Covariance Matrix Among Repeated Measures
Importance of Random Effects
Working With Matrices in R
Overview of Maximum Likelihood Estimation
Conceptual Overview
Maximum Likelihood and LM
Several Unknown Parameters
Exhaustive Search and Numerical Methods
Restricted Maximum Likelihood
Extracting the Log-Likelihood and the Deviance
Comparing Models
Maximum Likelihood and LMER
LMER Deviance Function
ML Standard Errors
Additional SE Details
Default lmer Output
Assumptions Regarding Missing Data
Additional Details of ML for LMER
Multimodel Inference and Akaike’s Information Criterion
Objects of Inference
Statistical Strategy
AIC and Predictive Accuracy
Extension to LMER
AIC Corrected
AICc and Effect Size
Delta
Weight of Evidence
Evidence Ratio
AICc and Multimodel Inference
Contrast With NHST
Example of Multimodel Analysis
Guidelines for Model Formulation
Example Set of Models
Bar Graphs of Results
Interpretation of Global Results
Details of Models
Comments Regarding the Multimodel Approach
Post Hoc Models
Example Write-up
Parametric Bootstrap of the Evidence Ratio
Performing the Parametric Bootstrap
Caveats Regarding the Parametric Bootstrap
Bayesian Information Criterion
Likelihood Ratio Test
Why Use the Likelihood Ratio Test
Fisher and Neyman-Pearson
Evaluation of Two Nested Models
Calibrating p-Values Based on Predictive Accuracy
Approaches to Testing Multiple Models
Step-Up Approach
Order of Testing
Comments on the Step-Up Approach
Top-Down Approach
Comparison of Approaches
Parametric Bootstrap
Comments on the Parametric Bootstrap
Planning a Study
Comment on the Procedure
Selecting Time Predictors
Selection of Time Transformations
Group-Level Selection of Time Transformations
Multimodel Inference
Analysis Without Static Predictors
Analysis With Static Predictors
Likelihood Ratio Test
Analysis Without Static Predictors
Analysis With Static Predictors
Cautions Concerning Group-Level Selection
Subject-Level Selection of Time Transformations
Level Polynomial Model
Missing Data
Subject-Level Fits
Pooled Measures of Fit
Clustering of Subject Curves
Selecting Random Effects
Automatic Selection of Random Effects
Random Effects and Variance Components
Restricted Maximum Likelihood
Random Effects and Correlated Data
Descriptive Methods
OLS Estimates
Examining Residuals
Residuals and Normality
Inferential Methods
Likelihood Ratio Test
AICc
Variance Components and Static Predictors
Predicted Random Effects
Evaluating the Normality Assumption
Predicted Values for an Individual
Extending Linear Mixed Effects Regression
Graphing Fitted Curves
Static Predictors With Multiple Levels
Evaluating Sets of Dummy Variables
Evaluating Individual Dummy Variables
Interactions Among Static Predictors
Static Predictor Interactions With lmer
Interpreting Interactions
Nonlinear Static Predictor Effects
Indexes of Absolute Effect Size in LMER
Alternative Indexes
Additional Transformations
Time Units and Variances
Transforming for Standardized Change
Standardizing and Compositing
Modeling Nonlinear Change
Data Set and Analysis Strategy
Global Versus Local Models
Polynomials
Mean-Corrected Polynomials
Orthogonal Polynomials
The poly Function
Polynomial Example
Alternatives to Polynomials
Trigonometric Functions
Fractional Polynomials
First-Order Fractional Polynomials
Second-Order Fractional Polynomials
Static Predictors
Caveats Regarding the Use of Fractional Polynomials
Spline Models
Linear Spline Models
Higher Order Regression Splines
Additional Details
Computing Orthogonal Polynomials
General Form of Fractional Polynomials
Advanced Topics
Dynamic Predictors
Dynamic Predictor as a Single Effect
Dynamic Predictor With a Time Variable
Multiple Response Variables
Reading and Mathematics
Analyzing Two Responses With lmer
Additional Levels of Nesting
Three-Level Model
Static Predictors in Three-Level Models
Appendix Soft Introduction to Matrix Algebra
A Matrices
A Transpose
A Matrix Addition
A Multiplication of a Matrix by a Scalar
A Matrix Multiplication
A Determinant
A Inverse
A Matrix Algebra and R Functions
References
Author Index
Subject Index

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