visualize.distributions: Visualize's Supported Distributions

Description Usage Format Author(s) Examples

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

1

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

Author(s)

James Balamuta

Examples

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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 Jan. 11, 2020, 9:21 a.m.