Health Science Statistics using R and R Commander

Robin Beaumont

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Format: Spiral bound

Publication date: January 7, 2015

Pages: 560 pages

ISBN: 9781907904318 Related titles:


Health Science Statistics using R and R Commander has been written for students, researchers and professionals who need a practical guide to the subject.

R is an open source statistical package that is finding favour in a wide variety of statistics applications. Initially R was the preserve of trained statisticians. However, it is increasingly being used with non-specialist audiences, at both postgraduate and senior undergraduate levels.

The book focuses on the graphical user interface, R Commander, which helps make R more user-friendly for the uninitiated. However, throughout the book, the R code behind R Commander is provided to allow the reader to program directly if required. The book provides both the practical skills and essential knowledge to enable the reader to perform their own statistical analyses in R and to interpret the results appropriately.

The book starts with introductory chapters which demonstrate how to install and run R and R Commander effortlessly. It then builds from introductory statistics chapters (calculating correlations and t tests) through to more complex areas (structural equation modelling, log linear regression etc.). 

Each chapter begins with a thorough introduction to the statistical technique under discussion. Then, working through real-life data, the reader is shown how to do their own analysis using R Commander, followed by a demonstration of how to do this analysis in R directly. The later chapters also show how to write up findings in the correct format. For specific analyses other free applications are introduced to supplement R (OpenEpi, Gpower and ?nyx). Throughout, the reader is given essential tips and advice to help get to grips with carrying out the analysis and intelligently reflecting on the output.

Health Science Statistics using R and R Commander is accompanied by an array of web-based material including:

  • additional online chapters
  • discussion board
  • R code for each chapter 
  • multiple choice questions
  • links to other resources including websites, blogs and tutorials

Health Science Statistics using R and R Commander is a comprehensive introduction to statistics in the health sciences combined with a hands-on practical guide to R (and related free software).

1. How this book works
2. Statistics and R – Setting the scene
3. R – What is it? Two ways to use it
4. Downloading and installing the R software – free!
5. Starting R
6. R Commander: a graphical front end to R
7. Packages: the apps
8. A quick tutorial – Analysing data shipped with R
9. A quick introduction to the R language: R

10. Basic statistical techniques
11. Summary statistics
12. Graphing Distributions of single variables: histograms and density plots
13. Histograms and density plots for subgroups defined by factor levels
14. Boxplots
15. Percentages for each category/factor level

16. Samples and populations
17. Comparing a sample mean to a population mean: Single sample t test
18. Comparing pre-post test means: Paired samples t test
19. Comparing 2 sample means: independent samples t test
20. Comparing pre-post test median difference: Wilcoxon Matched Pairs Statistic
21. Comparing 2 distributions: Mann-Whitney U Statistic
22. Comparing an observed proportion to a population value: The Binomial test
23. Several independent proportions compared with the average: Two way tables
24. Comparing several independent categories: Contingency tables
25. Measuring the degree to which two variable co-vary: Correlation
26. Measuring the influence of one variable on another: Regression

27. Health Statistics
28. Risk and odds ratios
29. Number needed to treat/harm (NNT/NNH)
30. Sensitivity, Specificity, predictive values and likelihood ratios
31. Levels of agreement: Kappa, Krippendorff and the ICC
32. Bland-Altman plots
33. Meta-analysis: the basics
34. Plotting survival over time: K-M (Kaplan-Meier) plots
35. Investigating effects upon survival over time: Cox PH regression
36. Graphical summaries of data: Aggregation
37. Paired nominal data: comparing proportions using McNemar’s test

38. Managing your data and R
39. Creating datasets and distributions in R Commander and R
40. Importing your data into R
41. Cutting and Pasting from Excel/Word to the R Data editor
42. Saving and exporting your work and data
43. R Script files (.R)
44. Manipulating variables (columns) in R Commander and R
45. Manipulating cases (rows) in R Commander and R
46. Expanding tables of counts into flat files
47. Installing non-CRANS packages
48. Workspaces, objects and history files
49. Developing R Code: Rstudio and NppToR

50. More ways of analysing your data
51. Mosaic and extended association plots
52. Multiway tables and Crosstabs
53. Resampling: Permutations, Jackknives and Bootstraps
54. Repeated measures: Mixed models and Gee
55. Sample size requirements
56. Confidence intervals for effect sizes: Noncentral distributions
57. Publication quality graphics

58. More Regression Techniques
59. Multiple Linear Regression: Measuring the influence of several variables on a continuous variable
60. Logistic regression: a binary outcome
61. Poisson (log-linear) Regression
62. Conditional Logistic Regression
63. Factorial Anova
64. Factor Analysis
65. Structural Equation Modelling (SEM)
66. Summary


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