vcd (version 0.1-3.5)

distplot: Diagnostic distribution plots

Description

Diagnostic distribution plots: poissonness, binomialness and negative bionomialness plots.

Usage

distplot(obj, type = c("poisson", "binomial", "nbinomial"),
         size = NULL, lambda = NULL, legend = TRUE, ylim = NULL,
         line.col = 2, conf.int = TRUE, conf.level = 0.95, main = NULL,
	 xlab = "Number of occurrences", ylab = "Distribution metameter", ...)

Arguments

obj

either a vector of counts, a 1-way table of frequencies of counts or a data frame or matrix with frequencies in the first column and the corresponding counts in the second column.

type

a character string indicating the distribution.

size

the size argument for the binomial distribution, only required if type is "binomial". If set to NULL, size is taken to be the maximum count.

lambda

parameter of the poisson distribution. If type is "poisson" and lambda is specified a leveled poissonness plot is produced.

legend

logical. Should a legend be plotted?.

ylim

limits for the y axis.

line.col

color for fitted line.

conf.int

logical. Should confidence intervals be plotted?.

conf.level

confidence level for confidence intervals.

main

a title for the plot.

xlab

a label for the x axis.

ylab

a label for the y axis.

...

further arguments passed to plot.

Details

distplot plots the number of occurrences (counts) against the distribution metameter of the specified distribution. If the distribution fits the data, the plot should show a straight line. See Friendly (2000) for details.

References

D. C. Hoaglin (1980), A poissonness plot, The American Statistican, 34, 146-149.

D. C. Hoaglin & J. W. Tukey (1985), Checking the shape of discrete distributions. In D. C. Hoaglin, F. Mosteller, J. W. Tukey (eds.), Exploring Data Tables, Trends and Shapes, chapter 9. John Wiley & Sons, New York.

Michael Friendly (2000), Visualizing Categorical Data. SAS Institute, Cary, NC.

Examples

## Simulated data examples:
dummy <- rnbinom(1000, size = 1.5, prob = 0.8)
distplot(dummy, type = "nbinomial")

## Real data examples:
data(HorseKicks)
data(Federalist)
data(Saxony)
distplot(HorseKicks, type = "poisson")
distplot(HorseKicks, type = "poisson", lambda = 0.61)
distplot(Federalist, type = "poisson")
distplot(Federalist, type = "nbinomial")
distplot(Saxony, type = "binomial", size = 12)