distribution_plot: Plot Density and Distribution Function With Markings

In ibawds: Functions and Datasets for the Data Science Course at IBAW

Plot Density and Distribution Function With Markings

Description

Create plots of the density and distribution functions of a probability distribution. It is possible to mark points and shade the area under the curve.

Usage

distribution_plot(
  fun,
  range,
  ...,
  points = NULL,
  var = "x",
  title = "Verteilungsfunktion",
  is_discrete = NULL
)

density_plot(
  fun,
  range,
  ...,
  from = NULL,
  to = NULL,
  points = NULL,
  var = "x",
  title = "Dichte",
  is_discrete = NULL
)

Arguments

fun	a density or distribution function that takes quantiles as its first argument.
range	numeric vector of length two giving the range of quantiles to be plotted.
...	further arguments that are passed to fun().
points	numeric vector giving quantiles where the function should be marked with a red dot (continuous) or a red bar (discrete).
var	character giving the name of the quantile variable. This is only used to label the axes.
title	character giving the title of the plot
is_discrete	logical indicating whether this is a discrete distribution. For discrete distributions, a bar plot is created. If omitted, the function tries to automatically determine, whether the distributions is discrete. In case this should fail, set this argument explicitly.
from, to	numeric values giving start and end of a range where the area under the density will be shaded (continuous) or the bars will be drawn in red (discrete). If only one of the two values is given, the shading will start at negative infinity or go until positive infinity, respectively.

Value

a ggplot object

Examples

# plot density of the normal distribution
density_plot(dnorm, c(-5, 7),
             mean = 1, sd = 2,
             to = 3)

# plot distribution function of the Poisson distribution
distribution_plot(ppois, c(0, 12),
                  lambda = 4,
                  points = c(2, 6, 10),
                  var = "y")

ibawds documentation built on June 17, 2022, 9:07 a.m.