{"product_id":"biostatistical-design-and-analysis-using-r-9781405190084","title":"Biostatistical Design and Analysis Using R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eR 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 modeli\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\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\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\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","brand":"John Wiley and Sons Ltd","offers":[{"title":"Default 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