plot.distribution: Plot the p.m.f, p.d.f or c.d.f. of a univariate distribution

In distributions3: Probability Distributions as S3 Objects

Plot the p.m.f, p.d.f or c.d.f. of a univariate distribution

Description

Plot method for an object inheriting from class "distribution". By default the probability density function (p.d.f.), for a continuous variable, or the probability mass function (p.m.f.), for a discrete variable, is plotted. The cumulative distribution function (c.d.f.) will be plotted if cdf = TRUE. Multiple functions are included in the plot if any of the parameter vectors in x has length greater than 1. See the argument all.

Usage

## S3 method for class 'distribution'
plot(
  x,
  cdf = FALSE,
  p = c(0.1, 99.9),
  len = 1000,
  all = FALSE,
  legend_args = list(),
  ...
)

Arguments

x	an object of class c("name", "distribution"), where "name" is the name of the distribution.
cdf	A logical scalar. If cdf = TRUE then the cumulative distribution function (c.d.f.) is plotted. Otherwise, the probability density function (p.d.f.), for a continuous variable, or the probability mass function (p.m.f.), for a discrete variable, is plotted.
p	A numeric vector. If xlim is not passed in ... then p is the fallback option for setting the range of values over which the p.m.f, p.d.f. or c.d.f is plotted. See Details.
len	An integer scalar. If x is a continuous distribution object then len is the number of values at which the p.d.f or c.d.f. is evaluated to produce the plot. The larger len is the smoother is the curve.
all	A logical scalar. If all = TRUE then a separate distribution is plotted for all the combinations of parameter values present in the parameter vectors present in x. These combinations are generated using expand.grid. If all = FALSE then the number of distributions plotted is equal to the maximum of the lengths of these parameter vectors, with shorter vectors recycled to this length if necessary using rep_len.
legend_args	A list of arguments to be passed to legend. In particular, the argument x (perhaps in conjunction with legend_args$y) can be used to set the position of the legend. If legend_args$x is not supplied then "bottomright" is used if cdf = TRUE and "topright" if cdf = FALSE.
...	Further arguments to be passed to plot, plot.ecdf and lines, such as xlim, ylim, xlab, ylab, main, lwd, lty, col, pch.

Details

If xlim is passed in ... then this determines the range of values of the variable to be plotted on the horizontal axis. If x is a discrete distribution object then the values for which the p.m.f. or c.d.f. is plotted is the smallest set of consecutive integers that contains both components of xlim. Otherwise, xlim is used directly.

If xlim is not passed in ... then the range of values spans the support of the distribution, with the following proviso: if the lower (upper) endpoint of the distribution is -Inf (Inf) then the lower (upper) limit of the plotting range is set to the p[1]\

If the name of x is a single upper case letter then that name is used to labels the axes of the plot. Otherwise, x and P(X = x) or f(x) are used.

A legend is included only if at least one of the parameter vectors in x has length greater than 1.

Plots of c.d.f.s are produced using calls to approxfun and plot.ecdf.

Value

An object with the same class as x, in which the parameter vectors have been expanded to contain a parameter combination for each function plotted.

Examples

B <- Binomial(20, 0.7)
plot(B)
plot(B, cdf = TRUE)

B2 <- Binomial(20, c(0.1, 0.5, 0.9))
plot(B2, legend_args = list(x = "top"))
x <- plot(B2, cdf = TRUE)
x$size
x$p

X <- Poisson(2)
plot(X)
plot(X, cdf = TRUE)

G <- Gamma(c(1, 3), 1:2)
plot(G)
plot(G, all = TRUE)
plot(G, cdf = TRUE)

C <- Cauchy()
plot(C, p = c(1, 99), len = 10000)
plot(C, cdf = TRUE, p = c(1, 99))

distributions3 documentation built on Sept. 30, 2024, 9:37 a.m.