bayestestR (version 0.7.2)

distribution: Empirical Distributions

Description

Generate a sequence of n-quantiles, i.e., a sample of size n with a near-perfect distribution.

Usage

distribution(type = "normal", ...)
distribution_normal(n, mean = 0, sd = 1, random = FALSE, ...)
distribution_binomial(n, size = 1, prob = 0.5, random = FALSE, ...)
distribution_cauchy(n, location = 0, scale = 1, random = FALSE, ...)
distribution_poisson(n, lambda = 1, random = FALSE, ...)
distribution_student(n, df, ncp, random = FALSE, ...)
distribution_chisquared(n, df, ncp = 0, random = FALSE, ...)
distribution_uniform(n, min = 0, max = 1, random = FALSE, ...)
distribution_beta(n, shape1, shape2, ncp = 0, random = FALSE, ...)
distribution_tweedie(n, xi = NULL, mu, phi, power = NULL, random = FALSE, ...)
distribution_gamma(n, shape, scale = 1, random = FALSE, ...)
distribution_custom(n, type = "norm", ..., random = FALSE)
distribution_mixture_normal(n, mean = c(-3, 3), sd = 1, random = FALSE, ...)
rnorm_perfect(n, mean = 0, sd = 1)

Arguments

type

Can be any of the names from base R's Distributions, like "cauchy", "pois" or "beta".

...

Arguments passed to or from other methods.

n

the number of observations

mean

vector of means.

sd

vector of standard deviations.

random

Generate near-perfect or random (simple wrappers for the base R r* functions) distributions.

size

number of trials (zero or more).

prob

probability of success on each trial.

location

location and scale parameters.

scale

location and scale parameters.

lambda

vector of (non-negative) means.

df

degrees of freedom (\(> 0\), maybe non-integer). df = Inf is allowed.

ncp

non-centrality parameter \(\delta\); currently except for rt(), only for abs(ncp) <= 37.62. If omitted, use the central t distribution.

min

lower and upper limits of the distribution. Must be finite.

max

lower and upper limits of the distribution. Must be finite.

shape1

non-negative parameters of the Beta distribution.

shape2

non-negative parameters of the Beta distribution.

xi

the value of \(\xi\) such that the variance is \(\mbox{var}[Y]=\phi\mu^{\xi}\)

mu

the mean

phi

the dispersion

power

a synonym for \(\xi\)

shape

shape and scale parameters. Must be positive, scale strictly.

Examples

# NOT RUN {
library(bayestestR)
x <- distribution(n = 10)
plot(density(x))

x <- distribution(type = "gamma", n = 100, shape = 2)
plot(density(x))
# }