- •brief contents
- •contents
- •preface
- •acknowledgments
- •about this book
- •What’s new in the second edition
- •Who should read this book
- •Roadmap
- •Advice for data miners
- •Code examples
- •Code conventions
- •Author Online
- •About the author
- •about the cover illustration
- •1 Introduction to R
- •1.2 Obtaining and installing R
- •1.3 Working with R
- •1.3.1 Getting started
- •1.3.2 Getting help
- •1.3.3 The workspace
- •1.3.4 Input and output
- •1.4 Packages
- •1.4.1 What are packages?
- •1.4.2 Installing a package
- •1.4.3 Loading a package
- •1.4.4 Learning about a package
- •1.5 Batch processing
- •1.6 Using output as input: reusing results
- •1.7 Working with large datasets
- •1.8 Working through an example
- •1.9 Summary
- •2 Creating a dataset
- •2.1 Understanding datasets
- •2.2 Data structures
- •2.2.1 Vectors
- •2.2.2 Matrices
- •2.2.3 Arrays
- •2.2.4 Data frames
- •2.2.5 Factors
- •2.2.6 Lists
- •2.3 Data input
- •2.3.1 Entering data from the keyboard
- •2.3.2 Importing data from a delimited text file
- •2.3.3 Importing data from Excel
- •2.3.4 Importing data from XML
- •2.3.5 Importing data from the web
- •2.3.6 Importing data from SPSS
- •2.3.7 Importing data from SAS
- •2.3.8 Importing data from Stata
- •2.3.9 Importing data from NetCDF
- •2.3.10 Importing data from HDF5
- •2.3.11 Accessing database management systems (DBMSs)
- •2.3.12 Importing data via Stat/Transfer
- •2.4 Annotating datasets
- •2.4.1 Variable labels
- •2.4.2 Value labels
- •2.5 Useful functions for working with data objects
- •2.6 Summary
- •3 Getting started with graphs
- •3.1 Working with graphs
- •3.2 A simple example
- •3.3 Graphical parameters
- •3.3.1 Symbols and lines
- •3.3.2 Colors
- •3.3.3 Text characteristics
- •3.3.4 Graph and margin dimensions
- •3.4 Adding text, customized axes, and legends
- •3.4.1 Titles
- •3.4.2 Axes
- •3.4.3 Reference lines
- •3.4.4 Legend
- •3.4.5 Text annotations
- •3.4.6 Math annotations
- •3.5 Combining graphs
- •3.5.1 Creating a figure arrangement with fine control
- •3.6 Summary
- •4 Basic data management
- •4.1 A working example
- •4.2 Creating new variables
- •4.3 Recoding variables
- •4.4 Renaming variables
- •4.5 Missing values
- •4.5.1 Recoding values to missing
- •4.5.2 Excluding missing values from analyses
- •4.6 Date values
- •4.6.1 Converting dates to character variables
- •4.6.2 Going further
- •4.7 Type conversions
- •4.8 Sorting data
- •4.9 Merging datasets
- •4.9.1 Adding columns to a data frame
- •4.9.2 Adding rows to a data frame
- •4.10 Subsetting datasets
- •4.10.1 Selecting (keeping) variables
- •4.10.2 Excluding (dropping) variables
- •4.10.3 Selecting observations
- •4.10.4 The subset() function
- •4.10.5 Random samples
- •4.11 Using SQL statements to manipulate data frames
- •4.12 Summary
- •5 Advanced data management
- •5.2 Numerical and character functions
- •5.2.1 Mathematical functions
- •5.2.2 Statistical functions
- •5.2.3 Probability functions
- •5.2.4 Character functions
- •5.2.5 Other useful functions
- •5.2.6 Applying functions to matrices and data frames
- •5.3 A solution for the data-management challenge
- •5.4 Control flow
- •5.4.1 Repetition and looping
- •5.4.2 Conditional execution
- •5.5 User-written functions
- •5.6 Aggregation and reshaping
- •5.6.1 Transpose
- •5.6.2 Aggregating data
- •5.6.3 The reshape2 package
- •5.7 Summary
- •6 Basic graphs
- •6.1 Bar plots
- •6.1.1 Simple bar plots
- •6.1.2 Stacked and grouped bar plots
- •6.1.3 Mean bar plots
- •6.1.4 Tweaking bar plots
- •6.1.5 Spinograms
- •6.2 Pie charts
- •6.3 Histograms
- •6.4 Kernel density plots
- •6.5 Box plots
- •6.5.1 Using parallel box plots to compare groups
- •6.5.2 Violin plots
- •6.6 Dot plots
- •6.7 Summary
- •7 Basic statistics
- •7.1 Descriptive statistics
- •7.1.1 A menagerie of methods
- •7.1.2 Even more methods
- •7.1.3 Descriptive statistics by group
- •7.1.4 Additional methods by group
- •7.1.5 Visualizing results
- •7.2 Frequency and contingency tables
- •7.2.1 Generating frequency tables
- •7.2.2 Tests of independence
- •7.2.3 Measures of association
- •7.2.4 Visualizing results
- •7.3 Correlations
- •7.3.1 Types of correlations
- •7.3.2 Testing correlations for significance
- •7.3.3 Visualizing correlations
- •7.4 T-tests
- •7.4.3 When there are more than two groups
- •7.5 Nonparametric tests of group differences
- •7.5.1 Comparing two groups
- •7.5.2 Comparing more than two groups
- •7.6 Visualizing group differences
- •7.7 Summary
- •8 Regression
- •8.1 The many faces of regression
- •8.1.1 Scenarios for using OLS regression
- •8.1.2 What you need to know
- •8.2 OLS regression
- •8.2.1 Fitting regression models with lm()
- •8.2.2 Simple linear regression
- •8.2.3 Polynomial regression
- •8.2.4 Multiple linear regression
- •8.2.5 Multiple linear regression with interactions
- •8.3 Regression diagnostics
- •8.3.1 A typical approach
- •8.3.2 An enhanced approach
- •8.3.3 Global validation of linear model assumption
- •8.3.4 Multicollinearity
- •8.4 Unusual observations
- •8.4.1 Outliers
- •8.4.3 Influential observations
- •8.5 Corrective measures
- •8.5.1 Deleting observations
- •8.5.2 Transforming variables
- •8.5.3 Adding or deleting variables
- •8.5.4 Trying a different approach
- •8.6 Selecting the “best” regression model
- •8.6.1 Comparing models
- •8.6.2 Variable selection
- •8.7 Taking the analysis further
- •8.7.1 Cross-validation
- •8.7.2 Relative importance
- •8.8 Summary
- •9 Analysis of variance
- •9.1 A crash course on terminology
- •9.2 Fitting ANOVA models
- •9.2.1 The aov() function
- •9.2.2 The order of formula terms
- •9.3.1 Multiple comparisons
- •9.3.2 Assessing test assumptions
- •9.4 One-way ANCOVA
- •9.4.1 Assessing test assumptions
- •9.4.2 Visualizing the results
- •9.6 Repeated measures ANOVA
- •9.7 Multivariate analysis of variance (MANOVA)
- •9.7.1 Assessing test assumptions
- •9.7.2 Robust MANOVA
- •9.8 ANOVA as regression
- •9.9 Summary
- •10 Power analysis
- •10.1 A quick review of hypothesis testing
- •10.2 Implementing power analysis with the pwr package
- •10.2.1 t-tests
- •10.2.2 ANOVA
- •10.2.3 Correlations
- •10.2.4 Linear models
- •10.2.5 Tests of proportions
- •10.2.7 Choosing an appropriate effect size in novel situations
- •10.3 Creating power analysis plots
- •10.4 Other packages
- •10.5 Summary
- •11 Intermediate graphs
- •11.1 Scatter plots
- •11.1.3 3D scatter plots
- •11.1.4 Spinning 3D scatter plots
- •11.1.5 Bubble plots
- •11.2 Line charts
- •11.3 Corrgrams
- •11.4 Mosaic plots
- •11.5 Summary
- •12 Resampling statistics and bootstrapping
- •12.1 Permutation tests
- •12.2 Permutation tests with the coin package
- •12.2.2 Independence in contingency tables
- •12.2.3 Independence between numeric variables
- •12.2.5 Going further
- •12.3 Permutation tests with the lmPerm package
- •12.3.1 Simple and polynomial regression
- •12.3.2 Multiple regression
- •12.4 Additional comments on permutation tests
- •12.5 Bootstrapping
- •12.6 Bootstrapping with the boot package
- •12.6.1 Bootstrapping a single statistic
- •12.6.2 Bootstrapping several statistics
- •12.7 Summary
- •13 Generalized linear models
- •13.1 Generalized linear models and the glm() function
- •13.1.1 The glm() function
- •13.1.2 Supporting functions
- •13.1.3 Model fit and regression diagnostics
- •13.2 Logistic regression
- •13.2.1 Interpreting the model parameters
- •13.2.2 Assessing the impact of predictors on the probability of an outcome
- •13.2.3 Overdispersion
- •13.2.4 Extensions
- •13.3 Poisson regression
- •13.3.1 Interpreting the model parameters
- •13.3.2 Overdispersion
- •13.3.3 Extensions
- •13.4 Summary
- •14 Principal components and factor analysis
- •14.1 Principal components and factor analysis in R
- •14.2 Principal components
- •14.2.1 Selecting the number of components to extract
- •14.2.2 Extracting principal components
- •14.2.3 Rotating principal components
- •14.2.4 Obtaining principal components scores
- •14.3 Exploratory factor analysis
- •14.3.1 Deciding how many common factors to extract
- •14.3.2 Extracting common factors
- •14.3.3 Rotating factors
- •14.3.4 Factor scores
- •14.4 Other latent variable models
- •14.5 Summary
- •15 Time series
- •15.1 Creating a time-series object in R
- •15.2 Smoothing and seasonal decomposition
- •15.2.1 Smoothing with simple moving averages
- •15.2.2 Seasonal decomposition
- •15.3 Exponential forecasting models
- •15.3.1 Simple exponential smoothing
- •15.3.3 The ets() function and automated forecasting
- •15.4 ARIMA forecasting models
- •15.4.1 Prerequisite concepts
- •15.4.2 ARMA and ARIMA models
- •15.4.3 Automated ARIMA forecasting
- •15.5 Going further
- •15.6 Summary
- •16 Cluster analysis
- •16.1 Common steps in cluster analysis
- •16.2 Calculating distances
- •16.3 Hierarchical cluster analysis
- •16.4 Partitioning cluster analysis
- •16.4.2 Partitioning around medoids
- •16.5 Avoiding nonexistent clusters
- •16.6 Summary
- •17 Classification
- •17.1 Preparing the data
- •17.2 Logistic regression
- •17.3 Decision trees
- •17.3.1 Classical decision trees
- •17.3.2 Conditional inference trees
- •17.4 Random forests
- •17.5 Support vector machines
- •17.5.1 Tuning an SVM
- •17.6 Choosing a best predictive solution
- •17.7 Using the rattle package for data mining
- •17.8 Summary
- •18 Advanced methods for missing data
- •18.1 Steps in dealing with missing data
- •18.2 Identifying missing values
- •18.3 Exploring missing-values patterns
- •18.3.1 Tabulating missing values
- •18.3.2 Exploring missing data visually
- •18.3.3 Using correlations to explore missing values
- •18.4 Understanding the sources and impact of missing data
- •18.5 Rational approaches for dealing with incomplete data
- •18.6 Complete-case analysis (listwise deletion)
- •18.7 Multiple imputation
- •18.8 Other approaches to missing data
- •18.8.1 Pairwise deletion
- •18.8.2 Simple (nonstochastic) imputation
- •18.9 Summary
- •19 Advanced graphics with ggplot2
- •19.1 The four graphics systems in R
- •19.2 An introduction to the ggplot2 package
- •19.3 Specifying the plot type with geoms
- •19.4 Grouping
- •19.5 Faceting
- •19.6 Adding smoothed lines
- •19.7 Modifying the appearance of ggplot2 graphs
- •19.7.1 Axes
- •19.7.2 Legends
- •19.7.3 Scales
- •19.7.4 Themes
- •19.7.5 Multiple graphs per page
- •19.8 Saving graphs
- •19.9 Summary
- •20 Advanced programming
- •20.1 A review of the language
- •20.1.1 Data types
- •20.1.2 Control structures
- •20.1.3 Creating functions
- •20.2 Working with environments
- •20.3 Object-oriented programming
- •20.3.1 Generic functions
- •20.3.2 Limitations of the S3 model
- •20.4 Writing efficient code
- •20.5 Debugging
- •20.5.1 Common sources of errors
- •20.5.2 Debugging tools
- •20.5.3 Session options that support debugging
- •20.6 Going further
- •20.7 Summary
- •21 Creating a package
- •21.1 Nonparametric analysis and the npar package
- •21.1.1 Comparing groups with the npar package
- •21.2 Developing the package
- •21.2.1 Computing the statistics
- •21.2.2 Printing the results
- •21.2.3 Summarizing the results
- •21.2.4 Plotting the results
- •21.2.5 Adding sample data to the package
- •21.3 Creating the package documentation
- •21.4 Building the package
- •21.5 Going further
- •21.6 Summary
- •22 Creating dynamic reports
- •22.1 A template approach to reports
- •22.2 Creating dynamic reports with R and Markdown
- •22.3 Creating dynamic reports with R and LaTeX
- •22.4 Creating dynamic reports with R and Open Document
- •22.5 Creating dynamic reports with R and Microsoft Word
- •22.6 Summary
- •afterword Into the rabbit hole
- •appendix A Graphical user interfaces
- •appendix B Customizing the startup environment
- •appendix C Exporting data from R
- •Delimited text file
- •Excel spreadsheet
- •Statistical applications
- •appendix D Matrix algebra in R
- •appendix E Packages used in this book
- •appendix F Working with large datasets
- •F.1 Efficient programming
- •F.2 Storing data outside of RAM
- •F.3 Analytic packages for out-of-memory data
- •F.4 Comprehensive solutions for working with enormous datasets
- •appendix G Updating an R installation
- •G.1 Automated installation (Windows only)
- •G.2 Manual installation (Windows and Mac OS X)
- •G.3 Updating an R installation (Linux)
- •references
- •index
- •Symbols
- •Numerics
- •23.1 The lattice package
- •23.2 Conditioning variables
- •23.3 Panel functions
- •23.4 Grouping variables
- •23.5 Graphic parameters
- •23.6 Customizing plot strips
- •23.7 Page arrangement
- •23.8 Going further
500 CHAPTER 21 Creating a package
list to c("wmc", |
"list"). This is a critical step in creating generic functions for han- |
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dling the object. |
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Table 21.1 List object returned by the wmc() function |
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Component |
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Description |
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CALL |
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Function call |
data |
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Data frame containing the dependent and grouping variable |
sumstats |
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Data frame with groups as columns and n, median, and mad as rows |
kw |
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Five-component list containing the results of the Kruskal–Wallis test |
method |
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One-element character vector containing the method used to adjust p-values for |
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multiple comparisons |
wmc |
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Four-column data frame containing the multiple comparisons |
vnames |
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Variable names |
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Although the list provides all the information required, you’d rarely access the components directly. Instead, you can create generic print(), summary(), and plot() functions to present this information in more concise and meaningful ways. These generic functions are considered next.
21.2.2Printing the results
Most analytic functions of any breadth come with generic print() and summary() functions. print() provides basic or raw information about an object, and summary() provides more detailed or processed (summarized) information. A plot()function is frequently included when a plot makes sense in the given context.
Following the S3 OOP guidelines described in section 20.3.1, if an object has the class attribute "foo", then print(x) executes print.foo(x) if it exists or print.default(x) otherwise. The same goes for summary() and plot(). Because the oneway() function returns an object of class "oneway", you need to define print
.oneway(), summary.oneway(), and plot.oneway() functions. The print.oneway() function is given in listing 21.3.
For the life data, print(results) produces basic information about the multiple comparisons:
data: hlef by region
Multiple Comparisons (Wilcoxon Rank Sum Tests)
Probability Adjustment = holm
|
Group.1 |
Group.2 |
W |
p |
1 |
South North Central 28.0 0.008583 |
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2 |
South |
West 27.0 0.004738 |
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3 |
South |
Northeast 17.0 0.008583 |
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4 |
North Central |
West 63.5 |
1.000000 |
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5 |
North Central |
Northeast 42.0 |
1.000000 |
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6 |
West |
Northeast 54.5 |
1.000000 |
Developing the package |
501 |
An informative header is printed, followed by Wilcoxon statistics and adjusted p-values for each pair of groups (Group.1 with Group.2).
Listing 21.3 Contents of the print.R file
#' @title Print multiple comparisons #'
#' @description
#' \code{print.oneway} prints pairwise group comparisons. #'
#' @details
#' This function prints Wilcoxon pairwise multiple comparisons created #' by the \code{\link{oneway}} function.
#' |
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#' @param x an object of class \code{oneway}. |
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#' @param ... additional arguments passed to the function. |
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#' @method print oneway |
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#' @export |
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#' @return the input object is returned silently. |
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#' @author Rob Kabacoff <rkabacoff@@statmethods.net> |
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#' @examples |
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#' results <- oneway(hlef ~ region, life) |
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#' print(results) |
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print.oneway <- function(x, ...){ |
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if (!inherits(x, "oneway")) |
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b Checks input |
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stop("Object must be of class 'oneway'") |
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cat("data:", x$vnames[1], "by", x$vnames[2], "\n\n") |
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c Prints the |
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cat("Multiple Comparisons (Wilcoxon Rank Sum Tests)\n") |
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cat(paste("Probability Adjustment = ", x$method, "\n", sep="")) |
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header |
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print(x$wmc, ...) |
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} |
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d Prints the table |
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The header contains comments starting with #' that will be used by the roxygen2 package to create package documentation (see section 21.3). The inherits() function is used to make sure the submitted object has class "oneway" b. A set of cat() functions prints a description of the analysis c. (This could have been written as a single cat() function, but I thought the current code was easier to read.) Finally, print.default() is called to print the multiple comparisons d. The summary
.oneway() function is considered next.
21.2.3Summarizing the results
The summary() function produces more comprehensive and processed output than the print() function. For the healthy life-expectancy data, the summary(results) statement produces the following:
data: hlef on region
Omnibus Test
Kruskal-Wallis chi-squared = 17.8749, df = 3, p-value = 0.0004668
502 |
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CHAPTER 21 Creating a package |
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Descriptive Statistics |
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South North Central |
West Northeast |
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n |
16.000 |
12.00 |
13.0000 |
9.000 |
median 13.000 |
15.40 |
15.6000 |
15.700 |
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mad |
1.483 |
1.26 |
0.7413 |
0.593 |
Multiple Comparisons (Wilcoxon Rank Sum Tests)
Probability Adjustment = holm
|
Group.1 |
Group.2 |
W |
p |
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1 |
South |
North Central 28.0 |
0.008583 |
** |
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2 |
South |
West 27.0 0.004738 ** |
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3 |
South |
Northeast 17.0 0.008583 ** |
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4 |
North Central |
West 63.5 |
1.000000 |
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5 |
North Central |
Northeast 42.0 |
1.000000 |
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6 |
West |
Northeast 54.5 1.000000 |
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--- |
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Signif. codes: |
0 '***' 0.001 |
'**' |
0.01 '*' |
0.05 '.' 0.1 ' ' 1 |
The output includes the results of the Kruskal–Wallis test, descriptive statistics (sample sizes, median, and median absolute deviations) for each group, and the multiple comparisons. In addition, the multiple-comparison table is annotated with stars to highlight significant results. The code for the summary.oneway() function is given in the following listing.
Listing 21.4 Contents of the summary.R file
#' @title Summarize oneway nonparametric analyses #'
#' @description
#' \code{summary.oneway} summarizes the results of a oneway #' nonparametric analysis.
#'
#' @details
#' This function prints a summary of analyses produced by
#' the \code{\link{oneway}} function. This includes descriptive #' statistics by group, an omnibus Kruskal-Wallis test, and
#' Wilcoxon pairwise multiple comparisons. #'
#' @param object an object of class \code{oneway}. #' @param ... additional parameters.
#' @method summary oneway #' @export
#' @return the input object is returned silently.
#' @author Rob Kabacoff <rkabacoff@@statmethods.net> #' @examples
#' results <- oneway(hlef ~ region, life) #' summary(results)
summary.oneway <- function(object, ...){ if (!inherits(object, "oneway"))
stop("Object must be of class 'oneway'") if(!exists("digits")) digits <- 4L
kw <- object$kw wmc <- object$wmc
Developing the package |
503 |
cat("data:", object$vnames[1], "on", object$vnames[2], "\n\n")
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cat("Omnibus Test\n") |
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cat(paste("Kruskal-Wallis chi-squared = ", |
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round(kw$statistic,4), |
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b Kruskal–Wallis |
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", df = ", round(kw$parameter, 3), |
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", p-value = ", |
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test |
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format.pval(kw$p.value, digits = digits), |
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"\n\n", sep="")) |
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cat("Descriptive Statistics\n") |
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c Descriptive statistics |
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print(object$sumstats, ...) |
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wmc$stars <- " " |
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wmc$stars[wmc$p < |
.1] <- "." |
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d Table annotation |
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wmc$stars[wmc$p < |
.05] <- "*" |
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wmc$stars[wmc$p < |
.01] <- "**" |
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wmc$stars[wmc$p < .001] <- "***" |
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names(wmc)[which(names(wmc)=="stars")] <- " " |
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Pairwise |
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cat("\nMultiple Comparisons (Wilcoxon Rank Sum Tests)\n") |
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e |
cat(paste("Probability Adjustment = ", object$method, "\n", sep="")) |
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multiple |
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print(wmc, ...) |
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comparisons |
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cat("---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' |
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1\n") |
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} |
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The class of the object passed to the function must be "oneway" or an error is thrown. Notice that the input parameter in the print.oneway() function is called x, but in the summary() function it’s called object. I chose these names to be consistent with the argument names in the print.default() and summary.default() functions provided by the base R installation. After the informational details, the results of the Kruskal–Wallis test are printed b. The format.pval() function formats the p-value in the output.
Next, you print the descriptive statistics (n, median, mad) for each group c. Before printing the data frame of pairwise multiple comparisons, a column of stars is added d. This column serves as an annotation for the table and indicates the level of significance each test would achieve (.1, .05, .01, or .001). Nonsignificant results are represented by a blank (empty string). The statement
names(wmc)[which(names(wmc)==”stars”)] <- “ “
removes the column name for the annotation column. You could have used the statement
names(wmc)[5] <- " "
but that would break if the column order was changed in the future. The annotated results are printed e, and a key describing the meaning of the annotations is printed below the table.