mosaic (version 0.3-13)

distPlot: Plots of Discrete and Continuous Distributions

Description

Provides a simple way to generate plots of pdfs, probability mass functions, cdfs, probability histograms, and normal-quantile plots for distributions known to R.

Usage

distPlot(dist, params = list(),
    kind = c("density", "cdf", "qq", "histogram"),
    xlab = "", ylab = "", breaks = NULL, type,
    resolution = 5000, ...)

Arguments

dist

A string identifying the distribution. This should work with any distribution that has associated functions beginning with 'd', 'p', and 'q' (e.g, dnorm, pnorm

params

a list containing parameters for the distribution

kind

one of "density", "cdf", "qq", or "histogram" (or prefix of any of these)

xlab,ylab

as per other lattice functions

breaks

a vector of break points for bins of histograms, as in xhistogram

type

passed along to various lattice graphing functions

resolution

number of points to sample when generating the plots

...

other arguments passed along to lattice graphing routines

Details

distPlot determines whether the distribution is continuous or discrete by seeing if all the sampled quantiles are unique. A discrete random variable with many possible values could fool this algorithm and be considered continuous.

The plots are done referencing a data frame with variables x and y giving points on the graph of the pdf or pmf for the distribution. This can be useful in conjuction with the groups argument. See the examples.

Examples

Run this code

distPlot('norm')
distPlot('norm', type='h')
distPlot('norm', kind='cdf')
distPlot('norm', params=list(mean=100, sd=10), kind='cdf')
distPlot('exp', kind='histogram')
distPlot('binom', params=list( 25, .25))
distPlot('binom', params=list( 25, .25), xlim=c(-1,26) )
distPlot('binom', params=list( 25, .25), kind='cdf')
distPlot('beta', params=list( 3, 10), kind='density')
distPlot('beta', params=list( 3, 10), kind='cdf')
distPlot( "binom", params=list(35,.25), groups= y < dbinom(qbinom(0.05, 35, .25), 35,.25) )
distPlot( "binom", params=list(35,.25), groups= y < dbinom(qbinom(0.05, 35, .25), 35,.25), kind='hist')

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