Biostatistical Design and Analysis Using R

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.

Auteur
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.

Texte du rabat
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.

Contenu

Preface xv

R quick reference card xix

General key to statistical methods xxvii

1 Introduction to R 1

1.1 Why R? 1

1.2 Installing R 2

1.2.1 Windows 2

1.2.2 Unix/Linux 2

1.2.3 MacOSX 3

1.3 The R environment 3

1.3.1 The console (command line) 4

1.4 Object names 4

1.5 Expressions, Assignment and Arithmetic 5

1.6 R Sessions and workspaces 6

1.6.1 Cleaning up 6

1.6.2 Workspaces 7

1.6.3 Current working directory 7

1.6.4 Quitting R 8

1.7 Getting help 8

1.8 Functions 9

1.9 Precedence 10

1.10 Vectors - variables 11

1.10.1 Regular or patterned sequences 12

1.10.2 Character vectors 13

1.10.3 Factors 15

1.11 Matrices, lists and data frames 16

1.11.1 Matrices 16

1.11.2 Lists 17

1.11.3 Data frames - data sets 18

1.12 Object information and conversion 18

1.12.1 Object information 18

1.12.2 Object conversion 20

1.13 Indexing vectors, matrices and lists 20

1.13.1 Vector indexing 21

1.13.2 Matrix indexing 22

1.13.3 List indexing 23

1.14 Pattern matching and replacement (character search and replace) 24

1.14.1 grep - pattern searching 24

1.14.2 regexpr - position and length of match 25

1.14.3 gsub - pattern replacement 26

1.15 Data manipulation 26

1.15.1 Sorting 26

1.15.2 Formatting data 27

1.16 Functions that perform other functions repeatedly 28

1.16.1 Along matrix margins 29

1.16.2 By factorial groups 30

1.16.3 By objects 30

1.17 Programming in R 30

1.17.1 Grouped expressions 31

1.17.2 Conditional execution if and ifelse 31

1.17.3 Repeated execution looping 32

1.17.4 Writing functions 34

1.18 An introduction to the R graphical environment 35

1.18.1 The plot() function 36

1.18.2 Graphical devices 39

1.18.3 Multiple graphics devices 40

1.19 Packages 42

1.19.1 Manual package management 42

1.19.2 Loading packages 45

1.20 Working with scripts 45

1.21 Citing R in publications 46

1.22 Further reading 47

2 Datasets 48

2.1 Constructing data frames 48

2.2 Reviewingadataframe-fix() 49

2.3 Importing (reading) data 50

2.3.1 Import from text file 50

2.3.2 Importing from the clipboard 51

2.3.3 Import from other software 51

2.4 Exporting (writing) data 52

2.5 Saving and loading of R objects 53

2.6 Data frame vectors 54

2.6.1 Factor levels 54

2.7 Manipulating data sets 56

2.7.1 Subsets of data frames data frame indexing 56

2.7.2 The %in% matching operator 57

2.7.3 Pivot tables and aggregating datasets 58

2.7.4 Sorting datasets 58

2.7.5 Accessing and evaluating expressions within the context of a dataframe 59

2.7.6 Reshaping dataframes 59

2.8 Dummy data sets - generating random data 62

3 Introductory Statistical Principles 65

3.1 Distributions 66

3.1.1 The normal distribution 67

3.1.2 Log-normal distribution 68

3.2 Scale transformations 68

3.3 Measures of location 69

3.4 Measures of dispersion and variability 70

3.5 Measures of the precision of estimates - standard errors and confidence intervals 71

3.6 Degrees of freedom 73

3.7 Methods of estimation 73

3.7.1 Least squares (LS) 73

3.7.2 Maximum likelihood (ML) 74

3.8 Outliers 75

3.9 Further reading 75

4 Sampling and Experimental Design with R 76

4.1 Random sampling 76 4.2 Experimental design ...