vcd (version 1.1-1)

distplot: Diagnostic Distribution Plots

Description

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

Usage

distplot(x, type = c("poisson", "binomial", "nbinomial"),
  size = NULL, lambda = NULL, legend = TRUE, xlim = NULL, ylim = NULL,
  conf_int = TRUE, conf_level = 0.95, main = NULL,
  xlab = "Number of occurrences", ylab = "Distribution metameter",
  gp = gpar(cex = 0.5), name = "distplot", newpage = TRUE, pop = TRUE, ...)

Arguments

x

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 and negative binomial distribution. If set to NULL and type is "binomial", then size is taken to be the maximum count. If set to NULL and

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?

xlim

limits for the x axis.

ylim

limits for the y axis.

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.

gp

a "gpar" object controlling the grid graphical parameters of the points.

name

name of the plotting viewport.

newpage

logical. Should grid.newpage be called before plotting?

pop

logical. Should the viewport created be popped?

...

further arguments passed to grid.points.

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.

M. 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", size = 1)
distplot(Federalist, type = "nbinomial")
distplot(Saxony, type = "binomial", size = 12)