Biostatistical Design and Analysis Using R: A Practical Guide

✍ Scribed by Dr Murray Logan

• Publisher: Wiley-Blackwell
• Year: 2010
• Tongue: English
• Leaves: 577
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Biostatistical Design and Analysis Using R......Page 4
Contents......Page 8
Preface......Page 18
R quick reference card......Page 22
General key to statistical methods......Page 30
1.1 Why R?......Page 32
1.2.2 Unix/Linux......Page 33
1.3 The R environment......Page 34
1.4 Object names......Page 35
1.5 Expressions, Assignment and Arithmetic......Page 36
1.6.1 Cleaning up......Page 37
1.6.3 Current working directory......Page 38
1.7 Getting help......Page 39
1.8 Functions......Page 40
1.9 Precedence......Page 41
1.10 Vectors - variables......Page 42
1.10.1 Regular or patterned sequences......Page 43
1.10.2 Character vectors......Page 44
1.10.3 Factors......Page 46
1.11.1 Matrices......Page 47
1.11.2 Lists......Page 48
1.12.1 Object information......Page 49
1.13 Indexing vectors, matrices and lists......Page 51
1.13.1 Vector indexing......Page 52
1.13.2 Matrix indexing......Page 53
1.13.3 List indexing......Page 54
1.14.1 grep - pattern searching......Page 55
1.14.2 regexpr - position and length of match......Page 56
1.15.1 Sorting......Page 57
1.15.2 Formatting data......Page 58
1.16 Functions that perform other functions repeatedly......Page 59
1.16.1 Along matrix margins......Page 60
1.17 Programming in R......Page 61
1.17.2 Conditional execution – if and ifelse......Page 62
1.17.3 Repeated execution – looping......Page 63
1.17.4 Writing functions......Page 65
1.18 An introduction to the R graphical environment......Page 66
1.18.1 The plot() function......Page 67
1.18.2 Graphical devices......Page 70
1.18.3 Multiple graphics devices......Page 71
1.19.1 Manual package management......Page 73
1.20 Working with scripts......Page 76
1.21 Citing R in publications......Page 77
1.22 Further reading......Page 78
2.1 Constructing data frames......Page 79
2.2 Reviewing a data frame - fix()......Page 80
2.3.1 Import from text file......Page 81
2.3.3 Import from other software......Page 82
2.4 Exporting (writing) data......Page 83
2.5 Saving and loading of R objects......Page 84
2.6.1 Factor levels......Page 85
2.7.1 Subsets of data frames – data frame indexing......Page 87
2.7.2 The %in% matching operator......Page 88
2.7.4 Sorting datasets......Page 89
2.7.6 Reshaping dataframes......Page 90
2.8 Dummy data sets - generating random data......Page 93
3 Introductory statistical principles......Page 96
3.1 Distributions......Page 97
3.1.1 The normal distribution......Page 98
3.2 Scale transformations......Page 99
3.3 Measures of location......Page 100
3.4 Measures of dispersion and variability......Page 101
3.5 Measures of the precision of estimates - standard errors and confidence intervals......Page 102
3.7.1 Least squares (LS)......Page 104
3.7.2 Maximum likelihood (ML)......Page 105
3.9 Further reading......Page 106
4.1 Random sampling......Page 107
4.2.1 Fully randomized treatment allocation......Page 114
4.2.2 Randomized complete block treatment allocation......Page 115
5 Graphical data presentation......Page 116
5.1.1 The type parameter......Page 117
5.1.2 The xlim and ylim parameters......Page 118
5.1.5 The log parameter......Page 119
5.2 Graphical Parameters......Page 120
5.2.1 Plot dimensional and layout parameters......Page 121
5.2.2 Axis characteristics......Page 123
5.2.5 Plotting character parameter - pch......Page 124
5.2.6 Fonts......Page 127
5.2.8 Colors......Page 129
5.3.1 Adding points - points()......Page 130
5.3.2 Adding text within a plot - text()......Page 131
5.3.3 Adding text to plot margins - mtext()......Page 132
5.3.4 Adding a legend - legend()......Page 133
5.3.5 More advanced text formatting......Page 135
5.3.6 Adding axes - axis()......Page 138
5.3.7 Adding lines and shapes within a plot......Page 139
5.4.1 Identifying points - identify()......Page 144
5.5.1 Postscript - poscript() and pdf()......Page 145
5.6 Working with multiple graphical devices......Page 146
5.7.1 Histogram......Page 147
5.7.2 Density functions......Page 148
5.7.3 Q-Q plots......Page 149
5.7.4 Boxplots......Page 150
5.8.1 Scatterplots......Page 151
5.9.2 Boxplots for grouped means......Page 156
5.9.3 Interaction plots - means plots......Page 157
5.9.4 Bargraphs......Page 158
5.10.1 Mosaic plots......Page 159
5.11 Trellis graphics......Page 160
5.11.1 scales() parameters......Page 163
5.12 Further reading......Page 164
6.1 Hypothesis testing......Page 165
6.3 t-tests......Page 167
6.5 Statistical decision and power......Page 168
6.7 Further reading......Page 170
6.8 Key for simple hypothesis testing......Page 171
6.9 Worked examples of real biological data sets......Page 173
7 Introduction to Linear models......Page 182
7.1 Linear models......Page 183
7.2 Linear models in R......Page 185
7.3.1 Linear models with factorial variables......Page 187
7.3.2 Linear model hypothesis testing......Page 193
7.4 Comments about the importance of understanding the structure and parameterization of linear models......Page 195
8 Correlation and simple linear regression......Page 198
8.1 Correlation......Page 199
8.1.4 Robust correlation......Page 200
8.2 Simple linear regression......Page 201
8.2.2 Null hypotheses......Page 202
8.2.3 Assumptions......Page 203
8.2.5 Model I and II regression......Page 204
8.2.7 Robust regression......Page 207
8.2.8 Power and sample size determination......Page 208
8.4 Correlation and regression in R......Page 209
8.5 Further reading......Page 210
8.6 Key for correlation and regression......Page 211
8.7 Worked examples of real biological data sets......Page 215
9.1 Multiple linear regression......Page 239
9.3 Null hypotheses......Page 240
9.4 Assumptions......Page 241
9.5.1 Polynomial regression......Page 242
9.7 Model selection......Page 245
9.7.1 Model averaging......Page 246
9.8 Regression trees......Page 249
9.10 Key and analysis sequence for multiple and complex regression......Page 250
9.11 Worked examples of real biological data sets......Page 255
10.0.1 Fixed versus random factors......Page 285
10.2 Linear model......Page 286
10.3 Analysis of variance......Page 287
10.4 Assumptions......Page 289
10.6 Tests of trends and means comparisons......Page 290
10.8 ANOVA in R......Page 292
10.10 Key for single factor classification (ANOVA)......Page 293
10.11 Worked examples of real biological data sets......Page 296
11 Nested ANOVA......Page 314
11.1 Linear models......Page 315
11.2.2 Factor B - the nested factor......Page 316
11.4 Variance components......Page 317
11.6 Pooling denominator terms......Page 320
11.8 Linear mixed effects models......Page 321
11.10 Power and optimisation of resource allocation......Page 323
11.11.1 Error strata (aov)......Page 324
11.13 Key for nested ANOVA......Page 325
11.14 Worked examples of real biological data sets......Page 329
12 Factorial ANOVA......Page 344
12.2 Null hypotheses......Page 345
12.2.1 Model 1 - fixed effects......Page 346
12.2.2 Model 2 - random effects......Page 347
12.3 Analysis of variance......Page 348
12.3.1 Quasi F-ratios......Page 351
12.5 Planned and unplanned comparisons......Page 352
12.6.1 Missing observations......Page 353
12.6.2 Missing combinations - missing cells......Page 355
12.7 Robust factorial ANOVA......Page 356
12.10 Further reading......Page 358
12.11 Key for factorial ANOVA......Page 359
12.12 Worked examples of real biological data sets......Page 365
13 Unreplicated factorial designs – randomized block and simple repeated measures......Page 391
13.2 Null hypotheses......Page 394
13.3 Analysis of variance......Page 395
13.4 Assumptions......Page 396
13.4.1 Sphericity......Page 397
13.4.2 Block by treatment interactions......Page 399
13.6 Unbalanced un-replicated factorial designs......Page 401
13.10 Further reading......Page 402
13.11 Key for randomized block and simple repeated measures ANOVA......Page 403
13.12 Worked examples of real biological data sets......Page 407
14 Partly nested designs: split plot and complex repeated measures......Page 430
14.1.1 Factor A - the main between block treatment effect......Page 431
14.1.3 Factor C - the main within block treatment effect......Page 432
14.2.2 Two between (α, γ), one within (δ) block effect......Page 433
14.4 Assumptions......Page 434
14.6 Further reading......Page 439
14.7 Key for partly nested ANOVA......Page 440
14.8 Worked examples of real biological data sets......Page 444
15 Analysis of covariance (ANCOVA)......Page 479
15.2 Linear models......Page 481
15.3 Analysis of variance......Page 482
15.4 Assumptions......Page 483
15.4.1 Homogeneity of slopes......Page 484
15.4.2 Similar covariate ranges......Page 485
15.8 Key for ANCOVA......Page 486
15.9 Worked examples of real biological data sets......Page 488
16 Simple Frequency Analysis......Page 497
16.1 The chi-square statistic......Page 498
16.3 Contingency tables......Page 500
16.3.1 Odds ratios......Page 501
16.4 G-tests......Page 503
16.5 Small sample sizes......Page 504
16.7 Power analysis......Page 505
16.10 Key for Analysing frequencies......Page 506
16.11 Worked examples of real biological data sets......Page 508
17 Generalized linear models (GLM)......Page 514
17.2.1 Logistic model......Page 516
17.2.2 Null hypotheses......Page 518
17.2.4 Multiple logistic regression......Page 519
17.3.2 Log-linear Modelling......Page 520
17.4 Assumptions......Page 523
17.5 Generalized additive models (GAM’s) - non-parametric GLM......Page 524
17.6 GLM and R......Page 525
17.8 Key for GLM......Page 526
17.9 Worked examples of real biological data sets......Page 529
Bibliography......Page 562
R index......Page 566
Statistics index......Page 572

✦ Subjects

Библиотека;Компьютерная литература;R;