An R Companion to Linear Statistical Models

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.