{"product_id":"biostatistical-design-and-analysis-using-r-isbn-9781405190084","title":"Biostatistical Design and Analysis Using R","description":"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.  \u003cp\u003e\u003cb\u003eTopics covered include:\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003esimple hypothesis testing, graphing\u003c\/li\u003e \u003cli\u003eexploratory data analysis and graphical summaries\u003c\/li\u003e \u003cli\u003eregression (linear, multi and non-linear)\u003c\/li\u003e \u003cli\u003esimple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures)\u003c\/li\u003e \u003cli\u003efrequency analysis and generalized linear models.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eLinear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques.\u003c\/p\u003e \u003cp\u003eThe book is accompanied by a companion website \u003cb\u003ewww.wiley.com\/go\/logan\/r\u003c\/b\u003e 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.\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eR quick reference card xix\u003c\/p\u003e \u003cp\u003eGeneral key to statistical methods xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to R 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Why R? 1\u003c\/p\u003e \u003cp\u003e1.2 Installing R 2\u003c\/p\u003e \u003cp\u003e1.2.1 Windows 2\u003c\/p\u003e \u003cp\u003e1.2.2 Unix\/Linux 2\u003c\/p\u003e \u003cp\u003e1.2.3 MacOSX 3\u003c\/p\u003e \u003cp\u003e1.3 The R environment 3\u003c\/p\u003e \u003cp\u003e1.3.1 The console (command line) 4\u003c\/p\u003e \u003cp\u003e1.4 Object names 4\u003c\/p\u003e \u003cp\u003e1.5 Expressions, Assignment and Arithmetic 5\u003c\/p\u003e \u003cp\u003e1.6 R Sessions and workspaces 6\u003c\/p\u003e \u003cp\u003e1.6.1 Cleaning up 6\u003c\/p\u003e \u003cp\u003e1.6.2 Workspaces 7\u003c\/p\u003e \u003cp\u003e1.6.3 Current working directory 7\u003c\/p\u003e \u003cp\u003e1.6.4 Quitting R 8\u003c\/p\u003e \u003cp\u003e1.7 Getting help 8\u003c\/p\u003e \u003cp\u003e1.8 Functions 9\u003c\/p\u003e \u003cp\u003e1.9 Precedence 10\u003c\/p\u003e \u003cp\u003e1.10 Vectors - variables 11\u003c\/p\u003e \u003cp\u003e1.10.1 Regular or patterned sequences 12\u003c\/p\u003e \u003cp\u003e1.10.2 Character vectors 13\u003c\/p\u003e \u003cp\u003e1.10.3 Factors 15\u003c\/p\u003e \u003cp\u003e1.11 Matrices, lists and data frames 16\u003c\/p\u003e \u003cp\u003e1.11.1 Matrices 16\u003c\/p\u003e \u003cp\u003e1.11.2 Lists 17\u003c\/p\u003e \u003cp\u003e1.11.3 Data frames - data sets 18\u003c\/p\u003e \u003cp\u003e1.12 Object information and conversion 18\u003c\/p\u003e \u003cp\u003e1.12.1 Object information 18\u003c\/p\u003e \u003cp\u003e1.12.2 Object conversion 20\u003c\/p\u003e \u003cp\u003e1.13 Indexing vectors, matrices and lists 20\u003c\/p\u003e \u003cp\u003e1.13.1 Vector indexing 21\u003c\/p\u003e \u003cp\u003e1.13.2 Matrix indexing 22\u003c\/p\u003e \u003cp\u003e1.13.3 List indexing 23\u003c\/p\u003e \u003cp\u003e1.14 Pattern matching and replacement (character search and replace) 24\u003c\/p\u003e \u003cp\u003e1.14.1 grep - pattern searching 24\u003c\/p\u003e \u003cp\u003e1.14.2 regexpr - position and length of match 25\u003c\/p\u003e \u003cp\u003e1.14.3 gsub - pattern replacement 26\u003c\/p\u003e \u003cp\u003e1.15 Data manipulation 26\u003c\/p\u003e \u003cp\u003e1.15.1 Sorting 26\u003c\/p\u003e \u003cp\u003e1.15.2 Formatting data 27\u003c\/p\u003e \u003cp\u003e1.16 Functions that perform other functions repeatedly 28\u003c\/p\u003e \u003cp\u003e1.16.1 Along matrix margins 29\u003c\/p\u003e \u003cp\u003e1.16.2 By factorial groups 30\u003c\/p\u003e \u003cp\u003e1.16.3 By objects 30\u003c\/p\u003e \u003cp\u003e1.17 Programming in R 30\u003c\/p\u003e \u003cp\u003e1.17.1 Grouped expressions 31\u003c\/p\u003e \u003cp\u003e1.17.2 Conditional execution – if and ifelse 31\u003c\/p\u003e \u003cp\u003e1.17.3 Repeated execution – looping 32\u003c\/p\u003e \u003cp\u003e1.17.4 Writing functions 34\u003c\/p\u003e \u003cp\u003e1.18 An introduction to the R graphical environment 35\u003c\/p\u003e \u003cp\u003e1.18.1 The plot() function 36\u003c\/p\u003e \u003cp\u003e1.18.2 Graphical devices 39\u003c\/p\u003e \u003cp\u003e1.18.3 Multiple graphics devices 40\u003c\/p\u003e \u003cp\u003e1.19 Packages 42\u003c\/p\u003e \u003cp\u003e1.19.1 Manual package management 42\u003c\/p\u003e \u003cp\u003e1.19.2 Loading packages 45\u003c\/p\u003e \u003cp\u003e1.20 Working with scripts 45\u003c\/p\u003e \u003cp\u003e1.21 Citing R in publications 46\u003c\/p\u003e \u003cp\u003e1.22 Further reading 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Datasets 48\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Constructing data frames 48\u003c\/p\u003e \u003cp\u003e2.2 Reviewingadataframe-fix() 49\u003c\/p\u003e \u003cp\u003e2.3 Importing (reading) data 50\u003c\/p\u003e \u003cp\u003e2.3.1 Import from text file 50\u003c\/p\u003e \u003cp\u003e2.3.2 Importing from the clipboard 51\u003c\/p\u003e \u003cp\u003e2.3.3 Import from other software 51\u003c\/p\u003e \u003cp\u003e2.4 Exporting (writing) data 52\u003c\/p\u003e \u003cp\u003e2.5 Saving and loading of R objects 53\u003c\/p\u003e \u003cp\u003e2.6 Data frame vectors 54\u003c\/p\u003e \u003cp\u003e2.6.1 Factor levels 54\u003c\/p\u003e \u003cp\u003e2.7 Manipulating data sets 56\u003c\/p\u003e \u003cp\u003e2.7.1 Subsets of data frames – data frame indexing 56\u003c\/p\u003e \u003cp\u003e2.7.2 The %in% matching operator 57\u003c\/p\u003e \u003cp\u003e2.7.3 Pivot tables and aggregating datasets 58\u003c\/p\u003e \u003cp\u003e2.7.4 Sorting datasets 58\u003c\/p\u003e \u003cp\u003e2.7.5 Accessing and evaluating expressions within the context of a dataframe 59\u003c\/p\u003e \u003cp\u003e2.7.6 Reshaping dataframes 59\u003c\/p\u003e \u003cp\u003e2.8 Dummy data sets - generating random data 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Introductory Statistical Principles 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Distributions 66\u003c\/p\u003e \u003cp\u003e3.1.1 The normal distribution 67\u003c\/p\u003e \u003cp\u003e3.1.2 Log-normal distribution 68\u003c\/p\u003e \u003cp\u003e3.2 Scale transformations 68\u003c\/p\u003e \u003cp\u003e3.3 Measures of location 69\u003c\/p\u003e \u003cp\u003e3.4 Measures of dispersion and variability 70\u003c\/p\u003e \u003cp\u003e3.5 Measures of the precision of estimates - standard errors and confidence intervals 71\u003c\/p\u003e \u003cp\u003e3.6 Degrees of freedom 73\u003c\/p\u003e \u003cp\u003e3.7 Methods of estimation 73\u003c\/p\u003e \u003cp\u003e3.7.1 Least squares (LS) 73\u003c\/p\u003e \u003cp\u003e3.7.2 Maximum likelihood (ML) 74\u003c\/p\u003e \u003cp\u003e3.8 Outliers 75\u003c\/p\u003e \u003cp\u003e3.9 Further reading 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Sampling and Experimental Design with R 76\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Random sampling 76\u003c\/p\u003e \u003cp\u003e4.2 Experimental design 83\u003c\/p\u003e \u003cp\u003e4.2.1 Fully randomized treatment allocation 83\u003c\/p\u003e \u003cp\u003e4.2.2 Randomized complete block treatment allocation 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Graphical Data Presentation 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 The plot() function 86\u003c\/p\u003e \u003cp\u003e5.1.1 The type parameter 86\u003c\/p\u003e \u003cp\u003e5.1.2 The xlim and ylim parameters 87\u003c\/p\u003e \u003cp\u003e5.1.3 The xlab and ylab parameters 88\u003c\/p\u003e \u003cp\u003e5.1.4 The axes and ann parameters 88\u003c\/p\u003e \u003cp\u003e5.1.5 The log parameter 88\u003c\/p\u003e \u003cp\u003e5.2 Graphical Parameters 89\u003c\/p\u003e \u003cp\u003e5.2.1 Plot dimensional and layout parameters 90\u003c\/p\u003e \u003cp\u003e5.2.2 Axis characteristics 92\u003c\/p\u003e \u003cp\u003e5.2.3 Character sizes 93\u003c\/p\u003e \u003cp\u003e5.2.4 Line characteristics 93\u003c\/p\u003e \u003cp\u003e5.2.5 Plotting character parameter - pch 93\u003c\/p\u003e \u003cp\u003e5.2.6 Fonts 96\u003c\/p\u003e \u003cp\u003e5.2.7 Text orientation and justification 98\u003c\/p\u003e \u003cp\u003e5.2.8 Colors 98\u003c\/p\u003e \u003cp\u003e5.3 Enhancing and customizing plots with low-level plotting functions 99\u003c\/p\u003e \u003cp\u003e5.3.1 Adding points - points() 99\u003c\/p\u003e \u003cp\u003e5.3.2 Adding text within a plot - text() 100\u003c\/p\u003e \u003cp\u003e5.3.3 Adding text to plot margins - mtext() 101\u003c\/p\u003e \u003cp\u003e5.3.4 Adding a legend - legend() 102\u003c\/p\u003e \u003cp\u003e5.3.5 More advanced text formatting 104\u003c\/p\u003e \u003cp\u003e5.3.6 Adding axes - axis() 107\u003c\/p\u003e \u003cp\u003e5.3.7 Adding lines and shapes within a plot 108\u003c\/p\u003e \u003cp\u003e5.4 Interactive graphics 113\u003c\/p\u003e \u003cp\u003e5.4.1 Identifying points - identify() 113\u003c\/p\u003e \u003cp\u003e5.4.2 Retrieving coordinates - locator() 114\u003c\/p\u003e \u003cp\u003e5.5 Exporting graphics 114\u003c\/p\u003e \u003cp\u003e5.5.1 Postscript - poscript() and pdf() 114\u003c\/p\u003e \u003cp\u003e5.5.2 Bitmaps - jpeg() and png() 115\u003c\/p\u003e \u003cp\u003e5.5.3 Copying devices - dev.copy() 115\u003c\/p\u003e \u003cp\u003e5.6 Working with multiple graphical devices 115\u003c\/p\u003e \u003cp\u003e5.7 High-level plotting functions for univariate (single variable) data 116\u003c\/p\u003e \u003cp\u003e5.7.1 Histogram 116\u003c\/p\u003e \u003cp\u003e5.7.2 Density functions 117\u003c\/p\u003e \u003cp\u003e5.7.3 Q-Q plots 118\u003c\/p\u003e \u003cp\u003e5.7.4 Boxplots 119\u003c\/p\u003e \u003cp\u003e5.7.5 Rug charts 120\u003c\/p\u003e \u003cp\u003e5.8 Presenting relationships 120\u003c\/p\u003e \u003cp\u003e5.8.1 Scatterplots 120\u003c\/p\u003e \u003cp\u003e5.9 Presenting grouped data 125\u003c\/p\u003e \u003cp\u003e5.9.1 Boxplots 125\u003c\/p\u003e \u003cp\u003e5.9.2 Boxplots for grouped means 125\u003c\/p\u003e \u003cp\u003e5.9.3 Interaction plots - means plots 126\u003c\/p\u003e \u003cp\u003e5.9.4 Bargraphs 127\u003c\/p\u003e \u003cp\u003e5.9.5 Violin plots 128\u003c\/p\u003e \u003cp\u003e5.10 Presenting categorical data 128\u003c\/p\u003e \u003cp\u003e5.10.1 Mosaic plots 128\u003c\/p\u003e \u003cp\u003e5.10.2 Association plots 129\u003c\/p\u003e \u003cp\u003e5.11 Trellis graphics 129\u003c\/p\u003e \u003cp\u003e5.11.1 scales() parameters 132\u003c\/p\u003e \u003cp\u003e5.12 Further reading 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Simple Hypothesis Testing – One and Two Population Tests 134\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Hypothesis testing 134\u003c\/p\u003e \u003cp\u003e6.2 One- and two-tailed tests 136\u003c\/p\u003e \u003cp\u003e6.3 t-tests 136\u003c\/p\u003e \u003cp\u003e6.4 Assumptions 137\u003c\/p\u003e \u003cp\u003e6.5 Statistical decision and power 137\u003c\/p\u003e \u003cp\u003e6.6 Robust tests 139\u003c\/p\u003e \u003cp\u003e6.7 Further reading 139\u003c\/p\u003e \u003cp\u003e6.8 Key for simple hypothesis testing 140\u003c\/p\u003e \u003cp\u003e6.9 Worked examples of real biological data sets 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Introduction to Linear Models 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Linear models 152\u003c\/p\u003e \u003cp\u003e7.2 Linear models in R 154\u003c\/p\u003e \u003cp\u003e7.3 Estimating linear model parameters 156\u003c\/p\u003e \u003cp\u003e7.3.1 Linear models with factorial variables 156\u003c\/p\u003e \u003cp\u003e7.3.2 Linear model hypothesis testing 162\u003c\/p\u003e \u003cp\u003e7.4 Comments about the importance of understanding the structure and parameterization of linear models 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Correlation and Simple Linear Regression 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Correlation 168\u003c\/p\u003e \u003cp\u003e8.1.1 Product moment correlation coefficient 169\u003c\/p\u003e \u003cp\u003e8.1.2 Null hypothesis 169\u003c\/p\u003e \u003cp\u003e8.1.3 Assumptions 169\u003c\/p\u003e \u003cp\u003e8.1.4 Robust correlation 169\u003c\/p\u003e \u003cp\u003e8.1.5 Confidence ellipses 170\u003c\/p\u003e \u003cp\u003e8.2 Simple linear regression 170\u003c\/p\u003e \u003cp\u003e8.2.1 Linear model 171\u003c\/p\u003e \u003cp\u003e8.2.2 Null hypotheses 171\u003c\/p\u003e \u003cp\u003e8.2.3 Assumptions 172\u003c\/p\u003e \u003cp\u003e8.2.4 Multiple responses for each level of the predictor 173\u003c\/p\u003e \u003cp\u003e8.2.5 Model I and II regression 173\u003c\/p\u003e \u003cp\u003e8.2.6 Regression diagnostics 176\u003c\/p\u003e \u003cp\u003e8.2.7 Robust regression 176\u003c\/p\u003e \u003cp\u003e8.2.8 Power and sample size determination 177\u003c\/p\u003e \u003cp\u003e8.3 Smoothers and local regression 178\u003c\/p\u003e \u003cp\u003e8.4 Correlation and regression in R 178\u003c\/p\u003e \u003cp\u003e8.5 Further reading 179\u003c\/p\u003e \u003cp\u003e8.6 Key for correlation and regression 180\u003c\/p\u003e \u003cp\u003e8.7 Worked examples of real biological data sets 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Multiple and Curvilinear Regression 208\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Multiple linear regression 208\u003c\/p\u003e \u003cp\u003e9.2 Linear models 209\u003c\/p\u003e \u003cp\u003e9.3 Null hypotheses 209\u003c\/p\u003e \u003cp\u003e9.4 Assumptions 210\u003c\/p\u003e \u003cp\u003e9.5 Curvilinear models 211\u003c\/p\u003e \u003cp\u003e9.5.1 Polynomial regression 211\u003c\/p\u003e \u003cp\u003e9.5.2 Nonlinear regression 214\u003c\/p\u003e \u003cp\u003e9.5.3 Diagnostics 214\u003c\/p\u003e \u003cp\u003e9.6 Robust regression 214\u003c\/p\u003e \u003cp\u003e9.7 Model selection 214\u003c\/p\u003e \u003cp\u003e9.7.1 Model averaging 215\u003c\/p\u003e \u003cp\u003e9.7.2 Hierarchical partitioning 218\u003c\/p\u003e \u003cp\u003e9.8 Regression trees 218\u003c\/p\u003e \u003cp\u003e9.9 Further reading 219\u003c\/p\u003e \u003cp\u003e9.10 Key and analysis sequence for multiple and complex regression 219\u003c\/p\u003e \u003cp\u003e9.11 Worked examples of real biological data sets 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Single Factor Classification (ANOVA) 254\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.0.1 Fixed versus random factors 254\u003c\/p\u003e \u003cp\u003e10.1 Null hypotheses 255\u003c\/p\u003e \u003cp\u003e10.2 Linear model 255\u003c\/p\u003e \u003cp\u003e10.3 Analysis of variance 256\u003c\/p\u003e \u003cp\u003e10.4 Assumptions 258\u003c\/p\u003e \u003cp\u003e10.5 Robust classification (ANOVA) 259\u003c\/p\u003e \u003cp\u003e10.6 Tests of trends and means comparisons 259\u003c\/p\u003e \u003cp\u003e10.7 Power and sample size determination 261\u003c\/p\u003e \u003cp\u003e10.8 ANOVA in R 261\u003c\/p\u003e \u003cp\u003e10.9 Further reading 262\u003c\/p\u003e \u003cp\u003e10.10 Key for single factor classification (ANOVA) 262\u003c\/p\u003e \u003cp\u003e10.11 Worked examples of real biological data sets 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Nested ANOVA 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Linear models 284\u003c\/p\u003e \u003cp\u003e11.2 Null hypotheses 285\u003c\/p\u003e \u003cp\u003e11.2.1 Factor A - the main treatment effect 285\u003c\/p\u003e \u003cp\u003e11.2.2 Factor B - the nested factor 285\u003c\/p\u003e \u003cp\u003e11.3 Analysis of variance 286\u003c\/p\u003e \u003cp\u003e11.4 Variance components 286\u003c\/p\u003e \u003cp\u003e11.5 Assumptions 289\u003c\/p\u003e \u003cp\u003e11.6 Pooling denominator terms 289\u003c\/p\u003e \u003cp\u003e11.7 Unbalanced nested designs 290\u003c\/p\u003e \u003cp\u003e11.8 Linear mixed effects models 290\u003c\/p\u003e \u003cp\u003e11.9 Robust alternatives 292\u003c\/p\u003e \u003cp\u003e11.10 Power and optimisation of resource allocation 292\u003c\/p\u003e \u003cp\u003e11.11 Nested ANOVA in R 293\u003c\/p\u003e \u003cp\u003e11.11.1 Error strata (aov) 293\u003c\/p\u003e \u003cp\u003e11.11.2 Linear mixed effects models (lme and lmer) 294\u003c\/p\u003e \u003cp\u003e11.12 Further reading 294\u003c\/p\u003e \u003cp\u003e11.13 Key for nested ANOVA 294\u003c\/p\u003e \u003cp\u003e11.14 Worked examples of real biological data sets 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Factorial ANOVA 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Linear models 314\u003c\/p\u003e \u003cp\u003e12.2 Null hypotheses 314\u003c\/p\u003e \u003cp\u003e12.2.1 Model 1 - fixed effects 315\u003c\/p\u003e \u003cp\u003e12.2.2 Model 2 - random effects 316\u003c\/p\u003e \u003cp\u003e12.2.3 Model 3 - mixed effects 317\u003c\/p\u003e \u003cp\u003e12.3 Analysis of variance 317\u003c\/p\u003e \u003cp\u003e12.3.1 Quasi F-ratios 320\u003c\/p\u003e \u003cp\u003e12.3.2 Interactions and main effects tests 321\u003c\/p\u003e \u003cp\u003e12.4 Assumptions 321\u003c\/p\u003e \u003cp\u003e12.5 Planned and unplanned comparisons 321\u003c\/p\u003e \u003cp\u003e12.6 Unbalanced designs 322\u003c\/p\u003e \u003cp\u003e12.6.1 Missing observations 322\u003c\/p\u003e \u003cp\u003e12.6.2 Missing combinations - missing cells 324\u003c\/p\u003e \u003cp\u003e12.7 Robust factorial ANOVA 325\u003c\/p\u003e \u003cp\u003e12.8 Power and sample sizes 327\u003c\/p\u003e \u003cp\u003e12.9 Factorial ANOVA in R 327\u003c\/p\u003e \u003cp\u003e12.10 Further reading 327\u003c\/p\u003e \u003cp\u003e12.11 Key for factorial ANOVA 328\u003c\/p\u003e \u003cp\u003e12.12 Worked examples of real biological data sets 334\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Unreplicated Factorial Designs – Randomized Block and Simple Repeated Measures 360\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Linear models 363\u003c\/p\u003e \u003cp\u003e13.2 Null hypotheses 363\u003c\/p\u003e \u003cp\u003e13.2.1 Factor A - the main within block treatment effect 364\u003c\/p\u003e \u003cp\u003e13.2.2 Factor B - the blocking factor 364\u003c\/p\u003e \u003cp\u003e13.3 Analysis of variance 364\u003c\/p\u003e \u003cp\u003e13.4 Assumptions 365\u003c\/p\u003e \u003cp\u003e13.4.1 Sphericity 366\u003c\/p\u003e \u003cp\u003e13.4.2 Block by treatment interactions 368\u003c\/p\u003e \u003cp\u003e13.5 Specific comparisons 370\u003c\/p\u003e \u003cp\u003e13.6 Unbalanced un-replicated factorial designs 370\u003c\/p\u003e \u003cp\u003e13.7 Robust alternatives 371\u003c\/p\u003e \u003cp\u003e13.8 Power and blocking efficiency 371\u003c\/p\u003e \u003cp\u003e13.9 Unreplicated factorial ANOVA in R 371\u003c\/p\u003e \u003cp\u003e13.10 Further reading 371\u003c\/p\u003e \u003cp\u003e13.11 Key for randomized block and simple repeated measures ANOVA 372\u003c\/p\u003e \u003cp\u003e13.12 Worked examples of real biological data sets 376\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Partly Nested Designs: Split Plot and Complex Repeated Measures 399\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Null hypotheses 400\u003c\/p\u003e \u003cp\u003e14.1.1 \u003ci\u003eFactor A\u003c\/i\u003e - the main between block treatment effect 400\u003c\/p\u003e \u003cp\u003e14.1.2 \u003ci\u003eFactor B\u003c\/i\u003e - the blocking factor 401\u003c\/p\u003e \u003cp\u003e14.1.3 \u003ci\u003eFactor C\u003c\/i\u003e - the main within block treatment effect 401\u003c\/p\u003e \u003cp\u003e14.1.4 \u003ci\u003eAC interaction\u003c\/i\u003e - the within block interaction effect 402\u003c\/p\u003e \u003cp\u003e14.1.5 \u003ci\u003eBC interaction\u003c\/i\u003e - the within block interaction effect 402\u003c\/p\u003e \u003cp\u003e14.2 Linear models 402\u003c\/p\u003e \u003cp\u003e14.2.1 One between (α), one within (γ) block effect 402\u003c\/p\u003e \u003cp\u003e14.2.2 Two between (α, γ), one within (δ) block effect 402\u003c\/p\u003e \u003cp\u003e14.2.3 One between (α), two within (γ , δ) block effects 403\u003c\/p\u003e \u003cp\u003e14.3 Analysis of variance 403\u003c\/p\u003e \u003cp\u003e14.4 Assumptions 403\u003c\/p\u003e \u003cp\u003e14.5 Other issues 408\u003c\/p\u003e \u003cp\u003e14.5.1 Robust alternatives 408\u003c\/p\u003e \u003cp\u003e14.6 Further reading 408\u003c\/p\u003e \u003cp\u003e14.7 Key for partly nested ANOVA 409\u003c\/p\u003e \u003cp\u003e14.8 Worked examples of real biological data sets 413\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Analysis of Covariance (ANCOVA) 448\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Null hypotheses 450\u003c\/p\u003e \u003cp\u003e15.1.1 \u003ci\u003eFactor A\u003c\/i\u003e - the main treatment effect 450\u003c\/p\u003e \u003cp\u003e15.1.2 \u003ci\u003eFactor B\u003c\/i\u003e - the covariate effect 450\u003c\/p\u003e \u003cp\u003e15.2 Linear models 450\u003c\/p\u003e \u003cp\u003e15.3 Analysis of variance 451\u003c\/p\u003e \u003cp\u003e15.4 Assumptions 452\u003c\/p\u003e \u003cp\u003e15.4.1 Homogeneity of slopes 453\u003c\/p\u003e \u003cp\u003e15.4.2 Similar covariate ranges 454\u003c\/p\u003e \u003cp\u003e15.5 Robust ANCOVA 455\u003c\/p\u003e \u003cp\u003e15.6 Specific comparisons 455\u003c\/p\u003e \u003cp\u003e15.7 Further reading 455\u003c\/p\u003e \u003cp\u003e15.8 Key for ANCOVA 455\u003c\/p\u003e \u003cp\u003e15.9 Worked examples of real biological data sets 457\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Simple Frequency Analysis 466\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 The chi-square statistic 467\u003c\/p\u003e \u003cp\u003e16.1.1 Assumptions 469\u003c\/p\u003e \u003cp\u003e16.2 Goodness of fit tests 469\u003c\/p\u003e \u003cp\u003e16.2.1 Homogeneous frequencies tests 469\u003c\/p\u003e \u003cp\u003e16.2.2 Distributional conformity - Kolmogorov-Smirnov tests 469\u003c\/p\u003e \u003cp\u003e16.3 Contingency tables 469\u003c\/p\u003e \u003cp\u003e16.3.1 Odds ratios 470\u003c\/p\u003e \u003cp\u003e16.3.2 Residuals 472\u003c\/p\u003e \u003cp\u003e16.4 G-tests 472\u003c\/p\u003e \u003cp\u003e16.5 Small sample sizes 473\u003c\/p\u003e \u003cp\u003e16.6 Alternatives 474\u003c\/p\u003e \u003cp\u003e16.7 Power analysis 474\u003c\/p\u003e \u003cp\u003e16.8 Simple frequency analysis in R 475\u003c\/p\u003e \u003cp\u003e16.9 Further reading 475\u003c\/p\u003e \u003cp\u003e16.10 Key for Analysing frequencies 475\u003c\/p\u003e \u003cp\u003e16.11 Worked examples of real biological data sets 477\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Generalized Linear Models (GLM) 483\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Dispersion (over or under) 485\u003c\/p\u003e \u003cp\u003e17.2 Binary data - logistic (logit) regression 485\u003c\/p\u003e \u003cp\u003e17.2.1 Logistic model 485\u003c\/p\u003e \u003cp\u003e17.2.2 Null hypotheses 487\u003c\/p\u003e \u003cp\u003e17.2.3 Analysis of deviance 488\u003c\/p\u003e \u003cp\u003e17.2.4 Multiple logistic regression 488\u003c\/p\u003e \u003cp\u003e17.3 Count data - Poisson generalized linear models 489\u003c\/p\u003e \u003cp\u003e17.3.1 Poisson regression 489\u003c\/p\u003e \u003cp\u003e17.3.2 Log-linear Modelling 489\u003c\/p\u003e \u003cp\u003e17.4 Assumptions 492\u003c\/p\u003e \u003cp\u003e17.5 Generalized additive models (GAM’s) - non-parametric GLM 493\u003c\/p\u003e \u003cp\u003e17.6 GLM and R 494\u003c\/p\u003e \u003cp\u003e17.7 Further reading 495\u003c\/p\u003e \u003cp\u003e17.8 Key for GLM 495\u003c\/p\u003e \u003cp\u003e17.9 Worked examples of real biological data sets 498\u003c\/p\u003e \u003cp\u003eBibliography 531\u003c\/p\u003e \u003cp\u003eR index 535\u003c\/p\u003e \u003cp\u003eStatistics index 541\u003c\/p\u003e  \u003cp\u003e“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.”  (\u003ci\u003eBritish Ecological Society Bulletin\u003c\/i\u003e, 1 March 2012)\u003c\/p\u003e \u003cp\u003e\"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)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cb\u003eMurray Logan\u003c\/b\u003e 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.  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.  \u003cp\u003e\u003cb\u003eTopics covered include:\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003esimple hypothesis testing, graphing\u003c\/li\u003e \u003cli\u003eexploratory data analysis and graphical summaries\u003c\/li\u003e \u003cli\u003eregression (linear, multi and non-linear)\u003c\/li\u003e \u003cli\u003esimple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures)\u003c\/li\u003e \u003cli\u003efrequency analysis and generalized linear models.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eLinear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques.\u003c\/p\u003e \u003cp\u003eThe book is accompanied by a companion website \u003cb\u003ewww.wiley.com\/go\/logan\/r\u003c\/b\u003e 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.\u003c\/p\u003e","brand":"Wiley-Blackwell","offers":[{"title":"Default Title","offer_id":47988842266853,"sku":"NP9781405190084","price":90.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781405190084.jpg?v=1761781737","url":"https:\/\/k12savings.com\/es\/products\/biostatistical-design-and-analysis-using-r-isbn-9781405190084","provider":"K12savings","version":"1.0","type":"link"}