Biostatistical Design and Analysis Using R / Edition 1

Paperback (Print)
Buy New
Buy New from BN.com
$74.79
Used and New from Other Sellers
Used and New from Other Sellers
from $51.90
Usually ships in 1-2 business days
(Save 33%)
Other sellers (Paperback)
  • All (16) from $51.90   
  • New (11) from $59.52   
  • Used (5) from $51.90   

Overview

R — the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research.

Topics covered include:

  • simple hypothesis testing, graphing
  • exploratory data analysis and graphical summaries
  • regression (linear, multi and non-linear)
  • simple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures)
  • frequency analysis and generalized linear models.

Linear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques.

The book is accompanied by a companion website www.wiley.com/go/logan/r with an extensive set of resources comprising all R scripts and data sets used in the book, additional worked examples, the biology package, and other instructional materials and links.

Read More Show Less

Editorial Reviews

From the Publisher
“If you want to do more than just the basics then Biostatistical Design and Analysis using Ris an excellent guide, helping you climb the steep learning curve.” (British Ecological Society Bulletin, 1 March 2012)

"Overall, this is an excellent reference for biologists and biostatisticians; it is also a very good supplemental textbook for a graduate-level biostatistics course." (The Quarterly Review of Biology, 2011)

Read More Show Less

Product Details

  • ISBN-13: 9781405190084
  • Publisher: Wiley
  • Publication date: 5/17/2010
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 576
  • Sales rank: 1,231,687
  • Product dimensions: 6.70 (w) x 9.60 (h) x 1.20 (d)

Meet the Author

Murray Logan is a lecturer and researcher in the School of Biological Sciences, Monash University, Melbourne, Australia. He teaches a range of zoological and ecological courses in addition to biostatistical and R courses to undergraduate and graduate students. He also provides research design and analysis advice to a range of university, government and private organizations.

Read More Show Less

Table of Contents

Preface.

R quick reference card.

General key to statistical methods.

1 Introduction to R.

1.1 Why R?

1.2 Installing R.

1.3 The R environment.

1.4 Object names.

1.5 Expressions, Assignment and Arithmetic.

1.6 R Sessions and workspaces.

1.7 Getting help.

1.8 Functions.

1.9 Precedence.

1.10 Vectors - variables.

1.11 Matrices, lists and data frames.

1.12 Object information and conversion.

1.13 Indexing vectors, matrices and lists.

1.14 Pattern matching and replacement (character search and replace).

1.15 Data manipulation.

1.16 Functions that perform other functions repeatedly.

1.17 Programming in R.

1.18 An introduction to the R graphical environment.

1.19 Packages.

1.20 Working with scripts.

1.21 Citing R in publications.

1.22 Further reading.

2 Datasets.

2.1 Constructing data frames.

2.2 Reviewing a data frame - fix().

2.3 Importing (reading) data.

2.4 Exporting (writing) data.

2.5 Saving and loading of R objects.

2.6 Data frame vectors.

2.7 Manipulating data sets.

2.8 Dummy data sets - generating random data.

3 Introductory statistical principles.

3.1 Distributions.

3.2 Scale transformations.

3.3 Measures of location.

3.4 Measures of dispersion and variability.

3.5 Measures of the precision of estimates - standard errors and confidence intervals.

3.6 Degrees of freedom.

3.7 Methods of estimation.

3.8 Outliers.

3.9 Further reading.

4 Sampling and experimental design with R.

4.1 Random sampling.

4.2 Experimental design.

5 Graphical data presentation.

5.1 The plot() function.

5.2 Graphical Parameters.

5.3 Enhancing and customizing plots with low-level plotting functions.

5.4 Interactive graphics.

5.5 Exporting graphics.

5.6 Working with multiple graphical devices.

5.7 High-level plotting functions for univariate (single variable) data.

5.8 Presenting relationships.

5.9 Presenting grouped data.

5.10 Presenting categorical data.

5.11 Trellis graphics.

6 Simple hypothesis testing – one and two population tests.

6.1 Hypothesis testing.

6.2 One- and two-tailed tests.

6.3 t-tests.

6.4 Assumptions.

6.5 Statistical decision and power.

6.6 Robust tests.

6.7 Further reading.

6.8 Key for simple hypothesis testing.

6.9 Worked examples of real biological data sets.

7 Introduction to Linear models.

7.1 Linear models.

7.2 Linear models in R.

7.3 Estimating linear model parameters.

7.4 Comments about the importance of understanding the structure and parameterization of linear models.

8 Correlation and simple linear regression.

8.1 Correlation.

8.2 Simple linear regression.

8.3 Smoothers and local regression.

8.4 Correlation and regression in R.

8.5 Further reading.

8.6 Key for correlation and regression.

8.7 Worked examples of real biological data sets.

9 Multiple and curvilinear regression.

9.1 Multiple linear regression.

9.2 Linear models.

9.3 Null hypotheses.

9.4 Assumptions.

9.5 Curvilinear models.

9.6 Robust regression.

9.7 Model selection.

9.8 Regression trees.

9.9 Further reading.

9.10 Key and analysis sequence for multiple and complex regression.

9.11 Worked examples of real biological data sets.

10 Single factor classification (ANOVA).

10.0.1 Fixed versus random factors.

10.1 Null hypotheses.

10.2 Linear model.

10.3 Analysis of variance.

10.4 Assumptions.

10.5 Robust classification (ANOVA).

10.6 Tests of trends and means comparisons.

10.7 Power and sample size determination.

10.8 ANOVA in R.

10.9 Further reading.

10.10 Key for single factor classification (ANOVA).

10.11 Worked examples of real biological data sets.

11 Nested ANOVA.

11.1 Linear models.

11.2 Null hypotheses.

11.3 Analysis of variance.

11.4 Variance components.

11.5 Assumptions.

11.6 Pooling denominator terms.

11.7 Unbalanced nested designs.

11.8 Linear mixed effects models.

11.9 Robust alternatives.

11.10 Power and optimisation of resource allocation.

11.11 Nested ANOVA in R.

11.12 Further reading.

11.13 Key for nested ANOVA.

11.14 Worked examples of real biological data sets.

12 Factorial ANOVA.

12.1 Linear models.

12.2 Null hypotheses.

12.3 Analysis of variance.

12.4 Assumptions.

12.5 Planned and unplanned comparisons.

12.6 Unbalanced designs.

12.7 Robust factorial ANOVA.

12.8 Power and sample sizes.

12.9 Factorial ANOVA in R.

12.10 Further reading.

12.11 Key for factorial ANOVA.

12.12 Worked examples of real biological data sets.

13 Unreplicated factorial designs – randomized block and simple repeated measures.

13.1 Linear models.

13.2 Null hypotheses.

13.3 Analysis of variance.

13.4 Assumptions.

13.5 Specific comparisons.

13.6 Unbalanced un-replicated factorial designs.

13.7 Robust alternatives.

13.8 Power and blocking efficiency.

13.9 Unreplicated factorial ANOVA in R.

13.10 Further reading.

13.11 Key for randomized block and simple repeated measures ANOVA.

13.12 Worked examples of real biological data sets.

14 Partly nested designs: split plot and complex repeated measures.

14.1 Null hypotheses.

14.2 Linear models.

14.3 Analysis of variance.

14.4 Assumptions.

14.5 Other issues.

14.6 Further reading.

14.7 Key for partly nested ANOVA.

14.8 Worked examples of real biological data sets.

15 Analysis of covariance (ANCOVA).

15.1 Null hypotheses.

15.2 Linear models.

15.3 Analysis of variance.

15.4 Assumptions.

15.5 Robust ANCOVA.

15.6 Specific comparisons.

15.7 Further reading.

15.8 Key for ANCOVA.

15.9 Worked examples of real biological data sets.

16 Simple Frequency Analysis.

16.1 The chi-square statistic.

16.2 Goodness of fit tests.

16.3 Contingency tables.

16.4 G-tests.

16.5 Small sample sizes.

16.6 Alternatives.

16.7 Power analysis.

16.8 Simple frequency analysis in R.

16.9 Further reading.

16.10 Key for Analysing frequencies.

16.11 Worked examples of real biological data sets.

17 Generalized linear models (GLM).

17.1 Dispersion (over or under).

17.2 Binary data - logistic (logit) regression.

17.3 Count data - Poisson generalized linear models.

17.4 Assumptions.

17.5 Generalized additive models (GAM's) - non-parametric GLM.

17.6 GLM and R.

17.7 Further reading.

17.8 Key for GLM.

17.9 Worked examples of real biological data sets.

Bibliography.

R index.

Statistics index.

Companion website for this book: wiley.com/go/logan/r

Read More Show Less

Customer Reviews

Average Rating 4
( 2 )
Rating Distribution

5 Star

(1)

4 Star

(0)

3 Star

(1)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously
Sort by: Showing all of 2 Customer Reviews
  • Anonymous

    Posted September 20, 2012

    This is a really good book. I'm currently taking an Experimental

    This is a really good book. I'm currently taking an Experimental Design Class using R and the class is basically the same material that is on the book. The book has easy to follow instructions for coding and basic explanation for the concepts. I definitely recommend it to anyone that has no experience with R and is looking to learn or people that want to expand their knowledge and have a go to book in case of emergencies!

    1 out of 1 people found this review helpful.

    Was this review helpful? Yes  No   Report this review
  • Anonymous

    Posted May 15, 2013

    No text was provided for this review.

Sort by: Showing all of 2 Customer Reviews

If you find inappropriate content, please report it to Barnes & Noble
Why is this product inappropriate?
Comments (optional)