epdfPlot {EnvStats}  R Documentation 
Plot Empirical Probability Density Function
Description
Produces an empirical probability density function plot.
Usage
epdfPlot(x, discrete = FALSE, density.arg.list = NULL, plot.it = TRUE,
add = FALSE, epdf.col = "black", epdf.lwd = 3 * par("cex"), epdf.lty = 1,
curve.fill = FALSE, curve.fill.col = "cyan", ...,
type = ifelse(discrete, "h", "l"), main = NULL, xlab = NULL, ylab = NULL,
xlim = NULL, ylim = NULL)
Arguments
x 
numeric vector of observations. Missing ( 
discrete 
logical scalar indicating whether the assumed parent distribution of 
density.arg.list 
list with arguments to the 
plot.it 
logical scalar indicating whether to produce a plot or add to the current plot (see 
add 
logical scalar indicating whether to add the empirical pdf to the current plot
( 
epdf.col 
a numeric scalar or character string determining the color of the empirical pdf
line or points. The default value is 
epdf.lwd 
a numeric scalar determining the width of the empirical pdf line.
The default value is 
epdf.lty 
a numeric scalar determining the line type of the empirical pdf line.
The default value is 
curve.fill 
a logical scalar indicating whether to fill in the area below the empirical pdf
curve with the
color specified by 
curve.fill.col 
a numeric scalar or character string indicating what color to use to fill in the
area below the empirical pdf curve. The default value is

type , main , xlab , ylab , xlim , ylim , ... 
additional graphical parameters (see 
Details
When a distribution is discrete and can only take on a finite number of values,
the empirical pdf plot is the same as the standard relative frequency histogram;
that is, each bar of the histogram represents the proportion of the sample
equal to that particular number (or category). When a distribution is continuous,
the function epdfPlot
calls the R function density
to
compute the estimated probability density at a number of evenly spaced points
between the minimum and maximum values.
Value
epdfPlot
invisibly returns a list with the following components:
x 
numeric vector of ordered quantiles. 
f.x 
numeric vector of the associated estimated values of the pdf. 
Note
An empirical probability density function (epdf) plot is a graphical tool that can be used in conjunction with other graphical tools such as histograms and boxplots to assess the characteristics of a set of data.
Author(s)
Steven P. Millard (EnvStats@ProbStatInfo.com)
References
Chambers, J.M., W.S. Cleveland, B. Kleiner, and P.A. Tukey. (1983). Graphical Methods for Data Analysis. Duxbury Press, Boston, MA.
See the REFERENCES section in the help file for density
.
See Also
Empirical, pdfPlot
, ecdfPlot
,
cdfPlot
, cdfCompare
, qqPlot
.
Examples
# Using Reference Area TcCB data in EPA.94b.tccb.df,
# create a histogram of the logtransformed observations,
# then superimpose the empirical pdf plot.
dev.new()
log.TcCB < with(EPA.94b.tccb.df, log(TcCB[Area == "Reference"]))
hist(log.TcCB, freq = FALSE, xlim = c(2, 1),
col = "cyan", xlab = "log [ TcCB (ppb) ]",
ylab = "Relative Frequency",
main = "Reference Area TcCB with Empirical PDF")
epdfPlot(log.TcCB, add = TRUE)
#==========
# Generate 20 observations from a Poisson distribution with
# parameter lambda = 10, and plot the empirical PDF.
set.seed(875)
x < rpois(20, lambda = 10)
dev.new()
epdfPlot(x, discrete = TRUE)
#==========
# Clean up
#
rm(log.TcCB, x)
graphics.off()