Buch, Englisch, 440 Seiten, Format (B × H): 156 mm x 234 mm
Buch, Englisch, 440 Seiten, Format (B × H): 156 mm x 234 mm
ISBN: 978-1-032-93694-9
Verlag: Taylor & Francis
Taking advantage of both user-developed code and specialized functions, this second edition of An R Companion to Linear Statistical Models again targets two primary audiences: Those who are familiar with the introductory theory and applications of linear statistical models and who wish to learn how to use R in this area, or explore further ideas that might appear in this Companion; and those who are enrolled in an intermediate to advanced level course on linear statistical models for which R is the computational platform.
This Companion includes accessible introductions to writing R code as well as making use of functions through relevant examples. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates. Also included in this edition is a new part containing chapters that revisit the one-factor fixed-effects model from alternative points of view, and provide introductions to applying R to nonstandard linear contrasts, one-factor random-effects and repeated-measures designs, weighted least squares, and modelling with binary response data. Key Features
- Demonstrates how to create user-defined functions, and how to use pre-packaged functions from the Comprehensive R Archive Network (CRAN) as well as functions prepared specifically for this Companion.
- Has carefully documented accompanying R script files that follow along with the discussions in the book, and also contain additional exploratory code.
- Makes use of a relevant collection of examples to demonstrate both the statistical methods being discussed, as well as the R code used implement the methods.
- Provides detailed interpretations and explanations of graphical tools used, computed model parameter estimates, associated tests, and common “rules of thumb” used in interpreting graphs and computational output.
- Limits statistical and mathematical background theory to that which aids in following computational methods.
Zielgruppe
Academic
Autoren/Hrsg.
Weitere Infos & Material
Preface to the Second Edition Preface to the First Edition I Some R Basics 1 Getting Started 2 Working with Numbers 3 Working with Data Structures 4 Basic Plotting Functions 5 Automating Flow in Code II Linear Regression Models 6 Simple Linear Regression 7 Simple Remedies for Simple Regression 8 Multiple Linear Regression 9 Additional Diagnostics for Multiple Regression 10 Simple Remedies for Multiple Regression III Linear Models with Fixed-Effects Factors 11 One-Factor Fixed-Effects Models 12 One-Factor Fixed-Effects Models with Covariates 13 One-Factor Fixed-Effects Models with a Blocking Variable 14 Two-Factor Fixed-Effects Models 15 Two-Factor Models with CovariatesSimple Remedies for Fixed-Effects Models IV Snippets for the Curious 16 The One-Factor Fixed-Effects Model Revisited 17 Linear Contrasts 18 The One-Factor Random-Effects Model 19 Repeated Measures Designs 20 Weighted Least Squares 21 Binary Response Data Bibliography Index
