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# Remove most objects from the working environment
rm(list = ls())
options(stringsAsFactors = F)
pkg <- "npar_1.0.tar.gz"
loc <- "http://www.statmethods.net/RiA"
url <- paste(loc, pkg, sep="/")
download.file(url, pkg)
install.packages(pkg, repos=NULL, type="source")
# 21.1. Nonparametric analysis and the npar package
library(npar)
hist(life$hlef, xlab = "Healthy Life Expectancy (years) at Age 65",
main = "Distribution of Healthy Life Expectancy for Women",
col = "grey", breaks = 10) # figure 21.1
# figure 21.2
library(ggplot2)
ggplot(data = life, aes(x=region, y=hlef)) +
geom_point(size=3, color="darkgrey") +
labs(title = "Distribution of HLE Estimates by Region",
x="US Region", y="Healthy Life Expectancy at Age 65") +
theme_bw()
# 21.1.1. Comparing groups with the npar package
# code listing 21.1. Comparison of HLE estimates with the npar package
library(npar)
results <- oneway(hlef ~ region, life)
summary(results)
# data: hlef on region
#
# Omnibus Test
# Kruskal-Wallis chi-squared = 17.8749, df = 3, p-value = 0.0004668
#
# Descriptive Statistics
# South North Central West Northeast
# n 16.0000 12.00000 13.0000 9.00000
# median 13.0000 15.40000 15.6000 15.70000
# mad 1.4826 1.26021 0.7413 0.59304
#
# Multiple Comparisons (Wilcoxon Rank Sum Tests)
# Probability Adjustment = holm
# Group.1 Group.2 W p
# 1 South North Central 28.0 0.008583179 **
# 2 South West 27.0 0.004737844 **
# 3 South Northeast 17.0 0.008583179 **
# 4 North Central West 63.5 1.000000000
# 5 North Central Northeast 42.0 1.000000000
# 6 West Northeast 54.5 1.000000000
# ---
# Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(results, col="lightblue", main="Multiple Comparisons",
xlab="US Region",
ylab="Healthy Life Expectancy (years) at Age 65")
#==========================================================
# 21.2.1. Computing the statistics
# code listing 21.2. Contents of the oneway.R file
#' @title Nonparametric group comparisons
#'
#' @description
#' \code{oneway} computes nonparametric group comparisons, including an
#' omnibus test and post-hoc pairwise group comparisons.
#'
#' @details
#' This function computes an omnibus Kruskal-Wallis test that the
#' groups are equal, followed by all pairwise comparisons using
#' Wilcoxon Rank Sum tests. Exact Wilcoxon tests can be requested if
#' there are no ties on the dependent variable. The p-values are
#' adjusted for multiple comparisons using the \code{\link{p.adjust}}
#' function.
#'
#' @param formula an object of class formula, relating the dependent
#' variable to the grouping variable.
#' @param data a data frame containing the variables in the model.
#' @param exact logical. If \code{TRUE}, calculate exact Wilcoxon tests.
#' @param sort logical. If \code{TRUE}, sort groups by median dependent
#' variable values.
#' @param method method for correcting p-values for multiple comparisons.
#' @export
#' @return a list with 7 elements:
#' \item{CALL}{function call}
#' \item{data}{data frame containing the depending and grouping variable}
#' \item{sumstats}{data frame with descriptive statistics by group}
#' \item{kw}{results of the Kruskal-Wallis test}
#' \item{method}{method used to adjust p-values}
#' \item{wmc}{data frame containing the multiple comparisons}
#' \item{vnames}{variable names}
#' @author Rob Kabacoff <rkabacoff@@statmethods.net>
#' @examples
#' results <- oneway(hlef ~ region, life)
#' summary(results)
#' plot(results, col="lightblue", main="Multiple Comparisons",
#' xlab="US Region", ylab="Healthy Life Expectancy at Age 65")
# 1. Function call
oneway <- function(formula, data, exact=FALSE, sort=TRUE,
method=c("holm", "hochberg", "hommel", "bonferroni",
"BH", "BY", "fdr", "none")){
# 2. Checks arguments
if (missing(formula) || class(formula) != "formula" ||
length(all.vars(formula)) != 2)
stop("'formula' is missing or incorrect")
method <- match.arg(method)
# 3. Sets up data
df <- model.frame(formula, data)
y <- df[[1]]
g <- as.factor(df[[2]])
vnames <- names(df)
# 4. Reorders factor levels
if(sort) g <- reorder(g, y, FUN=median)
groups <- levels(g)
k <- nlevels(g)
# 5. Summary statistics
getstats <- function(x)(c(N = length(x), Median = median(x),
MAD = mad(x)))
sumstats <- t(aggregate(y, by=list(g), FUN=getstats)[2])
rownames(sumstats) <- c("n", "median", "mad")
colnames(sumstats) <- groups
# 6. Statistical tests
kw <- kruskal.test(formula, data)
wmc <- NULL
for (i in 1:(k-1)){
for (j in (i+1):k){
y1 <- y[g==groups[i]]
y2 <- y[g==groups[j]]
test <- wilcox.test(y1, y2, exact=exact)
r <- data.frame(Group.1=groups[i], Group.2=groups[j],
W=test$statistic[[1]], p=test$p.value)
# note the [[]] to return a single number
wmc <- rbind(wmc, r)
}
}
wmc$p <- p.adjust(wmc$p, method=method)
# 7.Return results
data <- data.frame(y, g)
names(data) <- vnames
results <- list(CALL = match.call(),
data=data,
sumstats=sumstats, kw=kw,
method=method, wmc=wmc, vnames=vnames)
class(results) <- c("oneway", "list")
return(results)
}
# 21.2.2. Printing the results
print(results)
# data: hlef by region
#
# Multiple Comparisons (Wilcoxon Rank Sum Tests)
# Probability Adjustment = holm
# Group.1 Group.2 W p
# 1 South North Central 28.0 0.008583179
# 2 South West 27.0 0.004737844
# 3 South Northeast 17.0 0.008583179
# 4 North Central West 63.5 1.000000000
# 5 North Central Northeast 42.0 1.000000000
# 6 West Northeast 54.5 1.000000000
# code listing 21.3. Contents of the print.R file
#' @title Print multiple comparisons
#'
#' @description
#' \code{print.oneway} prints pairwise group comparisons.
#'
#' @details
#' This function prints Wilcoxon pairwise multiple comparisons created
#' by the \code{\link{oneway}} function.
#'
#' @param x an object of class \code{oneway}.
#' @param ... additional arguments passed to the function.
#' @method print oneway
#' @export
#' @return the input object is returned silently.
#' @author Rob Kabacoff <rkabacoff@@statmethods.net>
#' @examples
#' results <- oneway(hlef ~ region, life)
#' print(results)
print.oneway <- function(x, ...){
# 1. Checks input
if (!inherits(x, "oneway"))
stop("Object must be of class 'oneway'")
# 2.Print the header
cat("data:", x$vnames[1], "by", x$vnames[2], "\n\n")
cat("Multiple Comparisons (Wilcoxon Rank Sum Tests)\n")
cat(paste("Probability Adjustment = ", x$method, "\n", sep=""))
# 3. print the table
print(x$wmc, ...)
}
# 21.2.3. Summarizing the results
summary(results)
# code lisitng 21.4. Contents of the summary.R file
#' @title Summarize oneway nonparametric analyses
#'
#' @description
#' \code{summary.oneway} summarizes the results of a oneway
#' nonparametric analysis.
#'
#' @details
#' This function prints a summary of analyses produced by
#' the \code{\link{oneway}} function. This includes descriptive
#' statistics by group, an omnibus Kruskal-Wallis test, and
#' Wilcoxon pairwise multiple comparisons.
#'
#' @param object an object of class \code{oneway}.
#' @param ... additional parameters.
#' @method summary oneway
#' @export
#' @return the input object is returned silently.
#' @author Rob Kabacoff <rkabacoff@@statmethods.net>
#' @examples
#' results <- oneway(hlef ~ region, life)
#' summary(results)
summary.oneway <- function(object, ...){
if (!inherits(object, "oneway"))
stop("Object must be of class 'oneway'")
if(!exists("digits")) digits <- 4L
kw <- object$kw
wmc <- object$wmc
cat("data:", object$vnames[1], "on", object$vnames[2], "\n\n")
# Kruskal-Wallis test
cat("Omnibus Test\n")
cat(paste("Kruskal-Wallis chi-squared = ",
round(kw$statistic,4),
", df = ", round(kw$parameter, 3),
", p-value = ",
format.pval(kw$p.value, digits = digits),
"\n\n", sep=""))
# Descriptive Statistics
cat("Descriptive Statistics\n")
print(object$sumstats, ...)
# Table annotation
wmc$stars <- " "
wmc$stars[wmc$p < .1] <- "."
wmc$stars[wmc$p < .05] <- "*"
wmc$stars[wmc$p < .01] <- "**"
wmc$stars[wmc$p < .001] <- "***"
names(wmc)[which(names(wmc)=="stars")] <- " "
# Pairwise multiple comparisons
cat("\nMultiple Comparisons (Wilcoxon Rank Sum Tests)\n")
cat(paste("Probability Adjustment = ", object$method, "\n", sep=""))
print(wmc, ...)
cat("---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' '
1\n")
}
# 21.2.4. Plotting the results
# code listing 21.5. Contents of the plot.R file
#' @title Plot nonparametric group comparisons
#'
#' @description
#' \code{plot.oneway} plots nonparametric group comparisons.
#'
#' @details
#' This function plots nonparametric group comparisons
#' created by the \code{\link{oneway}} function using
#' annotated side by side boxplots. Medians and
#' sample sizes are placed at the top of the chart.
#' The overall median is represented by a horizontal
#' dashed line.
#'
#' @param x an object of class \code{oneway}.
#' @param ... additional arguments passed to the
#' \code{\link{boxplot}} function.
#' @method plot oneway
#' @export
#' @return NULL
#' @author Rob Kabacoff <rkabacoff@@statmethods.net>
#' @examples
#' results <- oneway(hlef ~ region, life)
#' plot(results, col="lightblue", main="Multiple Comparisons",
#' xlab="US Region", ylab="Healthy Life Expectancy at Age 65")
plot.oneway <- function(x, ...){
# 1. Checks input
if (!inherits(x, "oneway"))
stop("Object must be of class 'oneway'")
# 2. Generates the box plots
data <- x$data
y <- data[,1]
g <- data[,2]
stats <- x$sumstats
lbl <- paste("md=", stats[2,], "\nn=", stats[1,], sep="")
opar <- par(no.readonly=TRUE)
par(mar=c(5,4,8,2))
boxplot(y~g, ...)
# 3. Annotates the plot
abline(h=median(y), lty=2, col="darkgrey")
axis(3, at=1:length(lbl), labels=lbl, cex.axis=.9)
par(opar)
}
# 21.2.5. Adding sample data to the package
# code listing 21.6. Creating the life data frame
region <- c(rep("North Central", 12), rep("Northeast", 9),
rep("South", 16), rep("West", 13))
state <- c("IL","IN","IA","KS","MI","MN","MO","NE","ND","OH","SD","WI",
"CT","ME","MA","NH","NJ","NY","PA","RI","VT","AL","AR","DE",
"FL","GA","KY","LA","MD","MS","NC","OK","SC","TN","TX","VA",
"WV","AK","AZ","CA","CO","HI","ID","MT","NV","NM","OR","UT",
"WA","WY")
hlem <- c(12.6,12.2,13.4,13.1,12.8,14.3,11.7,13.1,12.9,12.2,13.3,13.4,
14.3,13.5,13.8,14,12.9,13.6,12.8,13.1,13.9,10.3,11.6,13.5,
14.3,11.6,10.2,11.6,13.3,10.1,11.7,10.8,12,11.2,12.2,13.3,
10.3,13.3,13.7,13.8,14.3,15,13.1,13.4,12.8,13.1,13.9,14.3,14,
13.7)
hlef <- c(14.3,14.1,15.9,15.1,14.7,16.7,14,15.7,16,14,16.4,16.1,16.7,
15.7,15.9,16,14.8,15.3,14.8,15.6,16.2,11.7,12.7,15.7,16.4,
13.1,11.6,12.3,15.3,11.4,13.5,12.9,13.6,12.5,13.4,14.9,11.6,
14.9,16.3,15.5,16.2,17.3,15.1,15.6,14.5,14.7,16,15.7,16,15.2)
life <- data.frame(region=factor(region), state=factor(state), hlem, hlef)
save(life, file='life.rda')
# code listing 21.7. Contents of the life.R file
#' @title Healthy Life Expectancy at Age 65
#'
#' @description A dataset containing the healthy life expectancy (expected
#' years of life in good health) at age 65, by US state in 2007-2009.
#' Estimates are reported separately for men and women.
#'
#' @docType data
#' @keywords datasets
#' @name life
#' @usage life
#' @format A data frame with 50 rows and 4 variables. The variables
#' are as follows:
#' \describe{
#' \item{region}{A factor with 4 levels (North Central, Northeast,
#' South, West)}
#' \item{state}{A factor with the 2-letter ISO codes for the 50 US
#' states}
#' \item{hlem}{Healthy life expectancy for men in years}
#' \item{hlef}{Healthy life expectancy for women in years}
#' }
#' @source The \code{hlem} and \code{hlef} data were obtained from
#' the Center for Disease Control and Prevention
#' \emph{Morbidity and Mortality Weekly Report} at \url{
#' http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6228a1.htm?s_cid=mm6228a1_w}.
#' The \code{region} variable was added from the
#' \code{\link[datasets]{state.region}} dataset.
NULL
# 21.3. Creating the package documentation
# code listing 21.8. Contents of the npar.R file
#' Functions for nonparametric group comparisons.
#'
#' npar provides tools for calculating and visualizing
#' nonparametric differences among groups.
#'
#' @docType package
#' @name npar-package
#' @aliases npar
NULL
#... this file must end with a blank line after the NULL...
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