plot.distribution {distributions3}  R Documentation 
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
.
## S3 method for class 'distribution'
plot(
x,
cdf = FALSE,
p = c(0.1, 99.9),
len = 1000,
all = FALSE,
legend_args = list(),
...
)
x 
an object of class 
cdf 
A logical scalar. If 
p 
A numeric vector. If 
len 
An integer scalar. If 
all 
A logical scalar. If 
legend_args 
A list of arguments to be passed to

... 
Further arguments to be passed to 
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
.
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.
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))