Health Science Statistics using R and R Commander 3/e

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 Appendices Glossary Index