An R Companion to Linear Statistical Models

Description

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... Read more

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.

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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

Author(s)

Biography

Christopher Hay-Jahans is a professor of mathematics at the University of Alaska Southeast in Juneau, AK. He enjoys teaching all levels of mathematics and statistics and, more recently, he has been dabbling in mentoring undergraduate biomathematics research projects through annual IBA CURE Workshops.

An R Companion to Linear Statistical Models

Description

Table of Contents

Author(s)

Biography

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