Efnisyfirlit

• The R Book
• Preface
• 1 Getting Started
  • 1.1 How to use this book
    • 1.1.1 Beginner in both computing and statistics
    • 1.1.2 Student needing help with project work
    • 1.1.3 Done some R and some statistics, but keen to learn more of both
    • 1.1.4 Done regression and ANOVA, but want to learn more advanced statistical modelling
    • 1.1.5 Experienced in statistics, but a beginner in R
    • 1.1.6 Experienced in computing, but a beginner in R
    • 1.1.7 Familiar with statistics and computing, but need a friendly reference manual
  • 1.2 Installing R
  • 1.3 Running R
  • 1.4 The Comprehensive R Archive Network
    • 1.4.1 Manuals
    • 1.4.2 Frequently asked questions
    • 1.4.3 Contributed documentation
  • 1.5 Getting help in R
    • 1.5.1 Worked examples of functions
    • 1.5.2 Demonstrations of R functions
  • 1.6 Packages in R
    • 1.6.1 Contents of packages
    • 1.6.2 Installing packages
  • 1.7 Command line versus scripts
  • 1.8 Data editor
  • 1.9 Changing the look of the R screen
  • 1.10 Good housekeeping
  • 1.11 Linking to other computer languages
• 2 Essentials of the R Language
  • 2.1 Calculations
    • 2.1.1 Complex numbers in R
    • 2.1.2 Rounding
    • 2.1.3 Arithmetic
    • 2.1.4 Modulo and integer quotients
    • 2.1.5 Variable names and assignment
    • 2.1.6 Operators
    • 2.1.7 Integers
    • 2.1.8 Factors
  • 2.2 Logical operations
    • 2.2.1 TRUE and T with FALSE and F
    • 2.2.2 Testing for equality with real numbers
    • 2.2.3 Equality of floating point numbers using all.equal
    • 2.2.4 Summarizing differences between objects using all.equal
    • 2.2.5 Evaluation of combinations of TRUE and FALSE
    • 2.2.6 Logical arithmetic
  • 2.3 Generating sequences
    • 2.3.1 Generating repeats
    • 2.3.2 Generating factor levels
  • 2.4 Membership: Testing and coercing in R
  • 2.5 Missing values, infinity and things that are not numbers
    • 2.5.1 Missing values: NA
  • 2.6 Vectors and subscripts
    • 2.6.1 Extracting elements of a vector using subscripts
    • 2.6.2 Classes of vector
    • 2.6.3 Naming elements within vectors
    • 2.6.4 Working with logical subscripts
  • 2.7 Vector functions
    • 2.7.1 Obtaining tables of means using tapply
    • 2.7.2 The aggregate function for grouped summary statistics
    • 2.7.3 Parallel minima and maxima: pmin and pmax
    • 2.7.4 Summary information from vectors by groups
    • 2.7.5 Addresses within vectors
    • 2.7.6 Finding closest values
    • 2.7.7 Sorting, ranking and ordering
    • 2.7.8 Understanding the difference between unique and duplicated
    • 2.7.9 Looking for runs of numbers within vectors
    • 2.7.10 Sets: union, intersect and setdiff
  • 2.8 Matrices and arrays
    • 2.8.1 Matrices
    • 2.8.2 Naming the rows and columns of matrices
    • 2.8.3 Calculations on rows or columns of the matrix
    • 2.8.4 Adding rows and columns to the matrix
    • 2.8.5 The sweep function
    • 2.8.6 Applying functions with apply, sapply and lapply
    • 2.8.7 Using the max.col function
    • 2.8.8 Restructuring a multi-dimensional array using aperm
  • 2.9 Random numbers, sampling and shuffling
    • 2.9.1 The sample function
  • 2.10 Loops and repeats
    • 2.10.1 Creating the binary representation of a number
    • 2.10.2 Loop avoidance
    • 2.10.3 The slowness of loops
    • 2.10.4 Do not ‘grow’ data sets by concatenation or recursive function calls
    • 2.10.5 Loops for producing time series
  • 2.11 Lists
    • 2.11.1 Lists and lapply
    • 2.11.2 Manipulating and saving lists
  • 2.12 Text, character strings and pattern matching
    • 2.12.1 Pasting character strings together
    • 2.12.2 Extracting parts of strings
    • 2.12.3 Counting things within strings
    • 2.12.4 Upper- and lower-case text
    • 2.12.5 The match function and relational databases
    • 2.12.6 Pattern matching
    • 2.12.7 Dot . as the ‘anything’ character
    • 2.12.8 Substituting text within character strings
    • 2.12.9 Locations of a pattern within a vector using regexpr
    • 2.12.10 Using %in% and which
    • 2.12.11 More on pattern matching
    • 2.12.12 Perl regular expressions
    • 2.12.13 Stripping patterned text out of complex strings
  • 2.13 Dates and times in R
    • 2.13.1 Reading time data from files
    • 2.13.2 The strptime function
    • 2.13.3 The difftime function
    • 2.13.4 Calculations with dates and times
    • 2.13.5 The difftime and as.difftime functions
    • 2.13.6 Generating sequences of dates
    • 2.13.7 Calculating time differences between the rows of a dataframe
    • 2.13.8 Regression using dates and times
    • 2.13.9 Summary of dates and times in R
  • 2.14 Environments
    • 2.14.1 Using with rather than attach
    • 2.14.2 Using attach in this book
  • 2.15 Writing R functions
    • 2.15.1 Arithmetic mean of a single sample
    • 2.15.2 Median of a single sample
    • 2.15.3 Geometric mean
    • 2.15.4 Harmonic mean
    • 2.15.5 Variance
    • 2.15.6 Degrees of freedom
    • 2.15.7 Variance ratio test
    • 2.15.8 Using variance
    • 2.15.9 Deparsing: A graphics function for error bars
    • 2.15.10 The switch function
    • 2.15.11 The evaluation environment of a function
    • 2.15.12 Scope
    • 2.15.13 Optional arguments
    • 2.15.14 Variable numbers of arguments (...)
    • 2.15.15 Returning values from a function
    • 2.15.16 Anonymous functions
    • 2.15.17 Flexible handling of arguments to functions
    • 2.15.18 Structure of an object: str
  • 2.16 Writing from R to file
    • 2.16.1 Saving your work
    • 2.16.2 Saving history
    • 2.16.3 Saving graphics
    • 2.16.4 Saving data produced within R to disc
    • 2.16.5 Pasting into an Excel spreadsheet
    • 2.16.6 Writing an Excel readable file from R
  • 2.17 Programming tips
• 3 Data Input
  • 3.1 Data input from the keyboard
  • 3.2 Data input from files
    • 3.2.1 The working directory
    • 3.2.2 Data input using read.table
    • 3.2.3 Common errors when using read.table
    • 3.2.4 Separators and decimal points
    • 3.2.5 Data input directly from the web
  • 3.3 Input from files using scan
    • 3.3.1 Reading a dataframe with scan
    • 3.3.2 Input from more complex file structures using scan
  • 3.4 Reading data from a file using readLines
    • 3.4.1 Input a dataframe using readLines
    • 3.4.2 Reading non-standard files using readLines
  • 3.5 Warnings when you attach the dataframe
  • 3.6 Masking
  • 3.7 Input and output formats
  • 3.8 Checking files from the command line
  • 3.9 Reading dates and times from files
  • 3.10 Built-in data files
  • 3.11 File paths
  • 3.12 Connections
  • 3.13 Reading data from an external database
    • 3.13.1 Creating the DSN for your computer
    • 3.13.2 Setting up R to read from the database
• 4 Dataframes
  • 4.1 Subscripts and indices
  • 4.2 Selecting rows from the dataframe at random
  • 4.3 Sorting dataframes
  • 4.4 Using logical conditions to select rows from the dataframe
  • 4.5 Omitting rows containing missing values, NA
    • 4.5.1 Replacing NAs with zeros
  • 4.6 Using order and !duplicated to eliminate pseudoreplication
  • 4.7 Complex ordering with mixed directions
  • 4.8 A dataframe with row names instead of row numbers
  • 4.9 Creating a dataframe from another kind of object
  • 4.10 Eliminating duplicate rows from a dataframe
  • 4.11 Dates in dataframes
  • 4.12 Using the match function in dataframes
  • 4.13 Merging two dataframes
  • 4.14 Adding margins to a dataframe
  • 4.15 Summarizing the contents of dataframes
• 5 Graphics
  • 5.1 Plots with two variables
  • 5.2 Plotting with two continuous explanatory variables: Scatterplots
    • 5.2.1 Plotting symbols: pch
    • 5.2.2 Colour for symbols in plots
    • 5.2.3 Adding text to scatterplots
    • 5.2.4 Identifying individuals in scatterplots
    • 5.2.5 Using a third variable to label a scatterplot
    • 5.2.6 Joining the dots
    • 5.2.7 Plotting stepped lines
  • 5.3 Adding other shapes to a plot
    • 5.3.1 Placing items on a plot with the cursor, using the locator function
    • 5.3.2 Drawing more complex shapes with polygon
  • 5.4 Drawing mathematical functions
    • 5.4.1 Adding smooth parametric curves to a scatterplot
    • 5.4.2 Fitting non-parametric curves through a scatterplot
  • 5.5 Shape and size of the graphics window
  • 5.6 Plotting with a categorical explanatory variable
    • 5.6.1 Boxplots with notches to indicate significant differences
    • 5.6.2 Barplots with error bars
    • 5.6.3 Plots for multiple comparisons
    • 5.6.4 Using colour palettes with categorical explanatory variables
  • 5.7 Plots for single samples
    • 5.7.1 Histograms and bar charts
    • 5.7.2 Histograms
    • 5.7.3 Histograms of integers
    • 5.7.4 Overlaying histograms with smooth density functions
    • 5.7.5 Density estimation for continuous variables
    • 5.7.6 Index plots
    • 5.7.7 Time series plots
    • 5.7.8 Pie charts
    • 5.7.9 The stripchart function
    • 5.7.10 A plot to test for normality
  • 5.8 Plots with multiple variables
    • 5.8.1 The pairs function
    • 5.8.2 The coplot function
    • 5.8.3 Interaction plots
  • 5.9 Special plots
    • 5.9.1 Design plots
    • 5.9.2 Bubble plots
    • 5.9.3 Plots with many identical values
  • 5.10 Saving graphics to file
  • 5.11 Summary
• 6 Tables
  • 6.1 Tables of counts
  • 6.2 Summary tables
  • 6.3 Expanding a table into a dataframe
  • 6.4 Converting from a dataframe to a table
  • 6.5 Calculating tables of proportions with prop.table
  • 6.6 The scale function
  • 6.7 The expand.grid function
  • 6.8 The model.matrix function
  • 6.9 Comparing table and tabulate
• 7 Mathematics
  • 7.1 Mathematical functions
    • 7.1.1 Logarithmic functions
    • 7.1.2 Trigonometric functions
    • 7.1.3 Power laws
    • 7.1.4 Polynomial functions
    • 7.1.5 Gamma function
    • 7.1.6 Asymptotic functions
    • 7.1.7 Parameter estimation in asymptotic functions
    • 7.1.8 Sigmoid (S-shaped) functions
    • 7.1.9 Biexponential model
    • 7.1.10 Transformations of the response and explanatory variables
  • 7.2 Probability functions
  • 7.3 Continuous probability distributions
    • 7.3.1 Normal distribution
    • 7.3.2 The central limit theorem
    • 7.3.3 Maximum likelihood with the normal distribution
    • 7.3.4 Generating random numbers with exact mean and standard deviation
    • 7.3.5 Comparing data with a normal distribution
    • 7.3.6 Other distributions used in hypothesis testing
    • 7.3.7 The chi-squared distribution
    • 7.3.8 Fisher’s F distribution
    • 7.3.9 Student’s t distribution
    • 7.3.10 The gamma distribution
    • 7.3.11 The exponential distribution
    • 7.3.12 The beta distribution
    • 7.3.13 The Cauchy distribution
    • 7.3.14 The lognormal distribution
    • 7.3.15 The logistic distribution
    • 7.3.16 The log-logistic distribution
    • 7.3.17 The Weibull distribution
    • 7.3.18 Multivariate normal distribution
    • 7.3.19 The uniform distribution
    • 7.3.20 Plotting empirical cumulative distribution functions
  • 7.4 Discrete probability distributions
    • 7.4.1 The Bernoulli distribution
    • 7.4.2 The binomial distribution
    • 7.4.3 The geometric distribution
    • 7.4.4 The hypergeometric distribution
    • 7.4.5 The multinomial distribution
    • 7.4.6 The Poisson distribution
    • 7.4.7 The negative binomial distribution
    • 7.4.8 The Wilcoxon rank-sum statistic
  • 7.5 Matrix algebra
    • 7.5.1 Matrix multiplication
    • 7.5.2 Diagonals of matrices
    • 7.5.3 Determinant
    • 7.5.4 Inverse of a matrix
    • 7.5.5 Eigenvalues and eigenvectors
    • 7.5.6 Matrices in statistical models
    • 7.5.7 Statistical models in matrix notation
  • 7.6 Solving systems of linear equations using matrices
  • 7.7 Calculus
    • 7.7.1 Derivatives
    • 7.7.2 Integrals
    • 7.7.3 Differential equations
• 8 Classical Tests
  • 8.1 Single samples
    • 8.1.1 Data summary
    • 8.1.2 Plots for testing normality
    • 8.1.3 Testing for normality
    • 8.1.4 An example of single-sample data
  • 8.2 Bootstrap in hypothesis testing
  • 8.3 Skew and kurtosis
    • 8.3.1 Skew
    • 8.3.2 Kurtosis
  • 8.4 Two samples
    • 8.4.1 Comparing two variances
    • 8.4.2 Comparing two means
    • 8.4.3 Student’s t test
    • 8.4.4 Wilcoxon rank-sum test
  • 8.5 Tests on paired samples
  • 8.6 The sign test
  • 8.7 Binomial test to compare two proportions
  • 8.8 Chi-squared contingency tables
    • 8.8.1 Pearson’s chi-squared
    • 8.8.2 G test of contingency
    • 8.8.3 Unequal probabilities in the null hypothesis
    • 8.8.4 Chi-squared tests on table objects
    • 8.8.5 Contingency tables with small expected frequencies: Fisher’s exact test
  • 8.9 Correlation and covariance
    • 8.9.1 Data dredging
    • 8.9.2 Partial correlation
    • 8.9.3 Correlation and the variance of differences between variables
    • 8.9.4 Scale-dependent correlations
  • 8.10 Kolmogorov–Smirnov test
  • 8.11 Power analysis
  • 8.12 Bootstrap
• 9 Statistical Modelling
  • 9.1 First things first
  • 9.2 Maximum likelihood
  • 9.3 The principle of parsimony (Occam’s razor)
  • 9.4 Types of statistical model
  • 9.5 Steps involved in model simplification
    • 9.5.1 Caveats
    • 9.5.2 Order of deletion
  • 9.6 Model formulae in R
    • 9.6.1 Interactions between explanatory variables
    • 9.6.2 Creating formula objects
  • 9.7 Multiple error terms
  • 9.8 The intercept as parameter 1
  • 9.9 The update function in model simplification
  • 9.10 Model formulae for regression
  • 9.11 Box–Cox transformations
  • 9.12 Model criticism
  • 9.13 Model checking
    • 9.13.1 Heteroscedasticity
    • 9.13.2 Non-normality of errors
  • 9.14 Influence
  • 9.15 Summary of statistical models in R
  • 9.16 Optional arguments in model-fitting functions
    • 9.16.1 Subsets
    • 9.16.2 Weights
    • 9.16.3 Missing values
    • 9.16.4 Offsets
    • 9.16.5 Dataframes containing the same variable names
  • 9.17 Akaike’s information criterion
    • 9.17.1 AIC as a measure of the fit of a model
  • 9.18 Leverage
  • 9.19 Misspecified model
  • 9.20 Model checking in R
  • 9.21 Extracting information from model objects
    • 9.21.1 Extracting information by name
    • 9.21.2 Extracting information by list subscripts
    • 9.21.3 Extracting components of the model using $
    • 9.21.4 Using lists with models
  • 9.22 The summary tables for continuous and categorical explanatory variables
  • 9.23 Contrasts
    • 9.23.1 Contrast coefficients
    • 9.23.2 An example of contrasts in R
    • 9.23.3 A priori contrasts
  • 9.24 Model simplification by stepwise deletion
  • 9.25 Comparison of the three kinds of contrasts
    • 9.25.1 Treatment contrasts
    • 9.25.2 Helmert contrasts
    • 9.25.3 Sum contrasts
  • 9.26 Aliasing
  • 9.27 Orthogonal polynomial contrasts: contr.poly
  • 9.28 Summary of statistical modelling
• 10 Regression
  • 10.1 Linear regression
    • 10.1.1 The famous five in R
    • 10.1.2 Corrected sums of squares and sums of products
    • 10.1.3 Degree of scatter
    • 10.1.4 Analysis of variance in regression: SSY = SSR + SSE
    • 10.1.5 Unreliability estimates for the parameters
    • 10.1.6 Prediction using the fitted model
    • 10.1.7 Model checking
  • 10.2 Polynomial approximations to elementary functions
  • 10.3 Polynomial regression
  • 10.4 Fitting a mechanistic model to data
  • 10.5 Linear regression after transformation
  • 10.6 Prediction following regression
  • 10.7 Testing for lack of fit in a regression
  • 10.8 Bootstrap with regression
  • 10.9 Jackknife with regression
  • 10.10 Jackknife after bootstrap
  • 10.11 Serial correlation in the residuals
  • 10.12 Piecewise regression
  • 10.13 Multiple regression
    • 10.13.1 The multiple regression model
    • 10.13.2 Common problems arising in multiple regression
• 11 Analysis of Variance
  • 11.1 One-way ANOVA
    • 11.1.1 Calculations in one-way ANOVA
    • 11.1.2 Assumptions of ANOVA
    • 11.1.3 A worked example of one-way ANOVA
    • 11.1.4 Effect sizes
    • 11.1.5 Plots for interpreting one-way ANOVA
  • 11.2 Factorial experiments
  • 11.3 Pseudoreplication: Nested designs and split plots
    • 11.3.1 Split-plot experiments
    • 11.3.2 Mixed-effects models
    • 11.3.3 Fixed effect or random effect?
    • 11.3.4 Removing the pseudoreplication
    • 11.3.5 Derived variable analysis
  • 11.4 Variance components analysis
  • 11.5 Effect sizes in ANOVA: aov or lm?
  • 11.6 Multiple comparisons
  • 11.7 Multivariate analysis of variance
• 12 Analysis of Covariance
  • 12.1 Analysis of covariance in R
  • 12.2 ANCOVA and experimental design
  • 12.3 ANCOVA with two factors and one continuous covariate
  • 12.4 Contrasts and the parameters of ANCOVA models
  • 12.5 Order matters in summary.aov
• 13 Generalized Linear Models
  • 13.1 Error structure
  • 13.2 Linear predictor
  • 13.3 Link function
    • 13.3.1 Canonical link functions
  • 13.4 Proportion data and binomial errors
  • 13.5 Count data and Poisson errors
  • 13.6 Deviance: Measuring the goodness of fit of a GLM
  • 13.7 Quasi-likelihood
  • 13.8 The quasi family of models
  • 13.9 Generalized additive models
  • 13.10 Offsets
  • 13.11 Residuals
    • 13.11.1 Misspecified error structure
    • 13.11.2 Misspecified link function
  • 13.12 Overdispersion
  • 13.13 Bootstrapping a GLM
  • 13.14 Binomial GLM with ordered categorical variables
• 14 Count Data
  • 14.1 A regression with Poisson errors
  • 14.2 Analysis of deviance with count data
  • 14.3 Analysis of covariance with count data
  • 14.4 Frequency distributions
  • 14.5 Overdispersion in log-linear models
  • 14.6 Negative binomial errors
• 15 Count Data in Tables
  • 15.1 A two-class table of counts
  • 15.2 Sample size for count data
  • 15.3 A four-class table of counts
  • 15.4 Two-by-two contingency tables
  • 15.5 Using log-linear models for simple contingency tables
  • 15.6 The danger of contingency tables
  • 15.7 Quasi-Poisson and negative binomial models compared
  • 15.8 A contingency table of intermediate complexity
  • 15.9 Schoener’s lizards: A complex contingency table
  • 15.10 Plot methods for contingency tables
  • 15.11 Graphics for count data: Spine plots and spinograms
• 16 Proportion Data
  • 16.1 Analyses of data on one and two proportions
  • 16.2 Count data on proportions
  • 16.3 Odds
  • 16.4 Overdispersion and hypothesis testing
  • 16.5 Applications
    • 16.5.1 Logistic regression with binomial errors
    • 16.5.2 Estimating LD50 and LD90 from bioassay data
    • 16.5.3 Proportion data with categorical explanatory variables
  • 16.6 Averaging proportions
  • 16.7 Summary of modelling with proportion count data
  • 16.8 Analysis of covariance with binomial data
  • 16.9 Converting complex contingency tables to proportions
    • 16.9.1 Analysing Schoener’s lizards as proportion data
• 17 Binary Response Variables
  • 17.1 Incidence functions
  • 17.2 Graphical tests of the fit of the logistic to data
  • 17.3 ANCOVA with a binary response variable
  • 17.4 Binary response with pseudoreplication
• 18 Generalized Additive Models
  • 18.1 Non-parametric smoothers
  • 18.2 Generalized additive models
    • 18.2.1 Technical aspects
  • 18.3 An example with strongly humped data
  • 18.4 Generalized additive models with binary data
  • 18.5 Three-dimensional graphic output from gam
• 19 Mixed-Effects Models
  • 19.1 Replication and pseudoreplication
  • 19.2 The lme and lmer functions
    • 19.2.1 lme
    • 19.2.2 lmer
  • 19.3 Best linear unbiased predictors
  • 19.4 Designed experiments with different spatial scales: Split plots
  • 19.5 Hierarchical sampling and variance components analysis
  • 19.6 Mixed-effects models with temporal pseudoreplication
  • 19.7 Time series analysis in mixed-effects models
  • 19.8 Random effects in designed experiments
  • 19.9 Regression in mixed-effects models
  • 19.10 Generalized linear mixed models
    • 19.10.1 Hierarchically structured count data
• 20 Non-Linear Regression
  • 20.1 Comparing Michaelis–Menten and asymptotic exponential
  • 20.2 Generalized additive models
  • 20.3 Grouped data for non-linear estimation
  • 20.4 Non-linear time series models (temporal pseudoreplication)
  • 20.5 Self-starting functions
    • 20.5.1 Self-starting Michaelis–Menten model
    • 20.5.2 Self-starting asymptotic exponential model
    • 20.5.3 Self-starting logistic
    • 20.5.4 Self-starting four-parameter logistic
    • 20.5.5 Self-starting Weibull growth function
    • 20.5.6 Self-starting first-order compartment function
  • 20.6 Bootstrapping a family of non-linear regressions
• 21 Meta-Analysis
  • 21.1 Effect size
  • 21.2 Weights
  • 21.3 Fixed versus random effects
    • 21.3.1 Fixed-effect meta-analysis of scaled differences
    • 21.3.2 Random effects with a scaled mean difference
  • 21.4 Random-effects meta-analysis of binary data
• 22 Bayesian Statistics
  • 22.1 Background
  • 22.2 A continuous response variable
  • 22.3 Normal prior and normal likelihood
  • 22.4 Priors
    • 22.4.1 Conjugate priors
  • 22.5 Bayesian statistics for realistically complicated models
  • 22.6 Practical considerations
  • 22.7 Writing BUGS models
  • 22.8 Packages in R for carrying out Bayesian analysis
  • 22.9 Installing JAGS on your computer
  • 22.10 Running JAGS in R
  • 22.11 MCMC for a simple linear regression
  • 22.12 MCMC for a model with temporal pseudoreplication
  • 22.13 MCMC for a model with binomial errors
• 23 Tree Models
  • 23.1 Background
  • 23.2 Regression trees
  • 23.3 Using rpart to fit tree models
  • 23.4 Tree models as regressions
  • 23.5 Model simplification
  • 23.6 Classification trees with categorical explanatory variables
  • 23.7 Classification trees for replicated data
  • 23.8 Testing for the existence of humps
• 24 Time Series Analysis
  • 24.1 Nicholson’s blowflies
  • 24.2 Moving average
  • 24.3 Seasonal data
    • 24.3.1 Pattern in the monthly means
  • 24.4 Built-in time series functions
  • 24.5 Decompositions
  • 24.6 Testing for a trend in the time series
  • 24.7 Spectral analysis
  • 24.8 Multiple time series
  • 24.9 Simulated time series
  • 24.10 Time series models
• 25 Multivariate Statistics
  • 25.1 Principal components analysis
  • 25.2 Factor analysis
  • 25.3 Cluster analysis
    • 25.3.1 Partitioning
    • 25.3.2 Taxonomic use of kmeans
  • 25.4 Hierarchical cluster analysis
  • 25.5 Discriminant analysis
  • 25.6 Neural networks
• 26 Spatial Statistics
  • 26.1 Point processes
    • 26.1.1 Random points in a circle
  • 26.2 Nearest neighbours
    • 26.2.1 Tessellation
  • 26.3 Tests for spatial randomness
    • 26.3.1 Ripley’s K
    • 26.3.2 Quadrat-based methods
    • 26.3.3 Aggregated pattern and quadrat count data
    • 26.3.4 Counting things on maps
  • 26.4 Packages for spatial statistics
    • 26.4.1 The spatstat package
    • 26.4.2 The spdep package
    • 26.4.3 Polygon lists
  • 26.5 Geostatistical data
  • 26.6 Regression models with spatially correlated errors: Generalized least squares
  • 26.7 Creating a dot-distribution map from a relational database
• 27 Survival Analysis
  • 27.1 A Monte Carlo experiment
  • 27.2 Background
  • 27.3 The survivor function
  • 27.4 The density function
  • 27.5 The hazard function
  • 27.6 The exponential distribution
    • 27.6.1 Density function
    • 27.6.2 Survivor function
    • 27.6.3 Hazard function
  • 27.7 Kaplan–Meier survival distributions
  • 27.8 Age-specific hazard models
  • 27.9 Survival analysis in R
    • 27.9.1 Parametric models
    • 27.9.2 Cox proportional hazards model
    • 27.9.3 Cox’s proportional hazard or a parametric model?
  • 27.10 Parametric analysis
  • 27.11 Cox’s proportional hazards
  • 27.12 Models with censoring
    • 27.12.1 Parametric models
    • 27.12.2 Comparing coxph and survreg survival analysis
• 28 Simulation Models
  • 28.1 Temporal dynamics: Chaotic dynamics in population size
    • 28.1.1 Investigating the route to chaos
  • 28.2 Temporal and spatial dynamics: A simulated random walk in two dimensions
  • 28.3 Spatial simulation models
    • 28.3.1 Metapopulation dynamics
    • 28.3.2 Coexistence resulting from spatially explicit (local) density dependence
  • 28.4 Pattern generation resulting from dynamic interactions
• 29 Changing the Look of Graphics
  • 29.1 Graphs for publication
  • 29.2 Colour
    • 29.2.1 Palettes for groups of colours
    • 29.2.2 The RColorBrewer package
    • 29.2.3 Coloured plotting symbols with contrasting margins
    • 29.2.4 Colour in legends
    • 29.2.5 Background colours
    • 29.2.6 Foreground colours
    • 29.2.7 Different colours and font styles for different parts of the graph
    • 29.2.8 Full control of colours in plots
  • 29.3 Cross-hatching
  • 29.4 Grey scale
  • 29.5 Coloured convex hulls and other polygons
  • 29.6 Logarithmic axes
  • 29.7 Different font families for text
  • 29.8 Mathematical and other symbols on plots
  • 29.9 Phase planes
  • 29.10 Fat arrows
  • 29.11 Three-dimensional plots
  • 29.12 Complex 3D plots with wireframe
  • 29.13 An alphabetical tour of the graphics parameters
    • 29.13.1 Text justification, adj
    • 29.13.2 Annotation of graphs, ann
    • 29.13.3 Delay moving on to the next in a series of plots, ask
    • 29.13.4 Control over the axes, axis
    • 29.13.5 Background colour for plots, bg
    • 29.13.6 Boxes around plots, bty
    • 29.13.7 Size of plotting symbols using the character expansion function, cex
    • 29.13.8 Changing the shape of the plotting region, plt
    • 29.13.9 Locating multiple graphs in non-standard layouts using fig
    • 29.13.10 Two graphs with a common x scale but different y scales using fig
    • 29.13.11 The layout function
    • 29.13.12 Creating and controlling multiple screens on a single device
    • 29.13.13 Orientation of numbers on the tick marks, las
    • 29.13.14 Shapes for the ends and joins of lines, lend and ljoin
    • 29.13.15 Line types, lty
    • 29.13.16 Line widths, lwd
    • 29.13.17 Several graphs on the same page, mfrow and mfcol
    • 29.13.18 Margins around the plotting area, mar
    • 29.13.19 Plotting more than one graph on the same axes, new
    • 29.13.20 Two graphs on the same plot with different scales for their y axes
    • 29.13.21 Outer margins, oma
    • 29.13.22 Packing graphs closer together
    • 29.13.23 Square plotting region, pty
    • 29.13.24 Character rotation, srt
    • 29.13.25 Rotating the axis labels
    • 29.13.26 Tick marks on the axes
    • 29.13.27 Axis styles
  • 29.14 Trellis graphics
    • 29.14.1 Panel box-and-whisker plots
    • 29.14.2 Panel scatterplots
    • 29.14.3 Panel barplots
    • 29.14.4 Panels for conditioning plots
    • 29.14.5 Panel histograms
    • 29.14.6 Effect sizes
    • 29.14.7 More panel functions
• References and Further Reading
• Index

UM RAFBÆKUR Á HEIMKAUP.IS

Bókahillan þín er þitt svæði og þar eru bækurnar þínar geymdar. Þú kemst í bókahilluna þína hvar og hvenær sem er í tölvu eða snjalltæki. Einfalt og þægilegt!

Rafbók til eignar
Rafbók til eignar þarf að hlaða niður á þau tæki sem þú vilt nota innan eins árs frá því bókin er keypt.

Þú kemst í bækurnar hvar sem er
Þú getur nálgast allar raf(skóla)bækurnar þínar á einu augabragði, hvar og hvenær sem er í bókahillunni þinni. Engin taska, enginn kyndill og ekkert vesen (hvað þá yfirvigt).

Auðvelt að fletta og leita
Þú getur flakkað milli síðna og kafla eins og þér hentar best og farið beint í ákveðna kafla úr efnisyfirlitinu. Í leitinni finnur þú orð, kafla eða síður í einum smelli.

Glósur og yfirstrikanir
Þú getur auðkennt textabrot með mismunandi litum og skrifað glósur að vild í rafbókina. Þú getur jafnvel séð glósur og yfirstrikanir hjá bekkjarsystkinum og kennara ef þeir leyfa það. Allt á einum stað.

Hvað viltu sjá? / Þú ræður hvernig síðan lítur út
Þú lagar síðuna að þínum þörfum. Stækkaðu eða minnkaðu myndir og texta með multi-level zoom til að sjá síðuna eins og þér hentar best í þínu námi.



Fleiri góðir kostir
- Þú getur prentað síður úr bókinni (innan þeirra marka sem útgefandinn setur)
- Möguleiki á tengingu við annað stafrænt og gagnvirkt efni, svo sem myndbönd eða spurningar úr efninu
- Auðvelt að afrita og líma efni/texta fyrir t.d. heimaverkefni eða ritgerðir
- Styður tækni sem hjálpar nemendum með sjón- eða heyrnarskerðingu