visualize.distributions: Visualize's Supported Distributions

In visualize: Graph Probability Distributions with User Supplied Parameters and Statistics

Visualize's Supported Distributions

Description

All of visualize's supported distributions with their density, probability, and quantile functions. In addition, mean and variance functions are present. Other descriptors also exist and are documented below.

Usage

visualize.distributions

Format

Distributions are loaded with the following format:

type:	specify either "continuous" or "discrete"
	to direct the query to the right graph handler.
name:	specify the name of the distribution.
	In example, "Poisson Distribution."
	This is used in the main graph title.
variable:	specify the variable in probability statement.
	In example, P(z < 5).
	This is used in the probability subtitle.
varsymbols:	specify the variable symbols for distribution.
	In example, mu = 1 sd = 2.
	This is used in the distribution subtitle.
params:	specify the amount of params required for distribution.
	This is used in the first error handling check to ensure
	the correct number of params is supplied.
init(params, ...):	Function that generates the mean and variance
	of a distribution.
density(x, params, ncp = 0, lower.tail = TRUE, log = FALSE, ...):	Function that provides the density value using vectors of the
	quantiles from the distribution.
	This serves as a wrapper for ddistr_name.
probability(x, params, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...)	Function that provides the probability value
	using vectors of quantiles from the distribution.
	This serves as a wrapper for pdistr_name.
quantile(x, params, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...)	Function that provides the quantile value
	using vectors of probabilities from the distribution.
	This serves as a wrapper for qdistr_name.

The distributions currently available to use are:

Distribution	r Name	Distribution	r Name
Beta	beta	Lognormal*	lnorm
Binomial	binom	Negative Binomial	nbinom
Cauchy*	cauchy	Normal	norm
Chisquare	chisq	Poisson	pois
Exponential	exp	Student t*	t
F*	f	Uniform	unif
Gamma	gamma	Geometric	geom
Hypergeometric	hyper	Wilcoxon*	wilcox
Logistic*	logis

• denotes the distribution was added in v2.0.

Author(s)

James Balamuta

Examples

visualize.distributions = list(
  'beta' = list(
    type = "continuous",
    name = "Beta Distribution",
    variable = "b",
    varsymbols = c("\u03B1","\u03B2"),
    params = 2,
    init  = function(params, ...) {
      shape1 = params[[1]]; shape2 = params[[2]]
      if(shape1 <= 0 || shape2 <= 0) stop("Error: Need alpha, beta  > 0")
      mean = shape1 / (shape1 + shape2)
      var = (shape1 * shape2)/((shape1 + shape2 + 1)*(shape1 + shape2)^2)
      c(mean, var)
    },
    density = function(x,params, ncp = 0, lower.tail = TRUE, log = FALSE, ...){
      if(params[[1]] <= 0 || params[[2]] <= 0) stop("Error: Need alpha, beta  > 0")
        dbeta(x,params[[1]], params[[2]], ncp = ncp, log = log)
    },
    probability = function(q,params, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...){
      if(params[[1]] <= 0 || params[[2]] <= 0) stop("Error: Need alpha, beta  > 0")
      pbeta(q,params[[1]], params[[2]], ncp = ncp, lower.tail = lower.tail, log.p = log.p)
    },
    quantile = function(p,params, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...){
      if(params[[1]] <= 0 || params[[2]] <= 0) stop("Error: Need alpha, beta  > 0")
      qbeta(p,params[[1]], params[[2]], ncp = ncp, lower.tail = lower.tail, log.p = log.p)
    }
  )
)

visualize documentation built on Nov. 13, 2023, 5:07 p.m.