PirateBay Download | long j longitudinal data analysis behavioral sciences using r 2011

long j longitudinal data analysis behavioral sciences using r 2011

Type:: E-books
Files:: 1
Size:: 36.2 MB
Uploaded On:: Feb. 9, 2024, 3:10 p.m.
Added By:: andryold1
Seeders:: 0
Leechers:: 0
Info Hash:: 5D77F5CF5BBDC67FE05DA70A5A13FDB2487CBC1B

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

Get This Torrent

Long J. Longitudinal Data Analysis for the Behavioral Sciences Using R 2011.pdf 36.2 MB

Details for: Long J. Longitudinal Data Analysis for the Behavioral Sciences Using R 2011

long j longitudinal data analysis behavioral sciences using r 2011

Similar Posts: