An R Companion to Linear Statistical Models

Christopher Hay-Jahans

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October 19, 2011 by Chapman and Hall/CRC
Professional - 372 Pages - 97 B/W Illustrations
ISBN 9781439873656 - CAT# K13410

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Features

  • Demonstrates the use of pre-packaged functions from the Comprehensive R Archive Network (CRAN), and how to create user-defined functions and programs
  • Shows examples of generating "grass-roots" code, which help remove the "black box" image of many pre-packaged functions
  • Applies methods to user-generated data, providing illustrations on how to create and test code for use on actual data
  • Provides detailed interpretations and explanations on computed model parameter estimates, and associated tests
  • Limits statistical theory to only that which is necessary for computations; common "rules of thumb" used in interpreting graphs and computational outputs are provided

Summary

Focusing on user-developed programming, An R Companion to Linear Statistical Models serves two audiences: those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays the foundation for later chapters.

This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates. It also demonstrates applications of several pre-packaged functions for complex computational procedures.