bayestestR: distribution – R documentation

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bayestestR

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_custom(n, type = "norm", ..., random = FALSE)

distribution_beta(n, shape1, shape2, ncp = 0, random = FALSE, ...)

distribution_binomial(n, size = 1, prob = 0.5, random = FALSE, ...)

distribution_binom(n, size = 1, prob = 0.5, random = FALSE, ...)

distribution_cauchy(n, location = 0, scale = 1, random = FALSE, ...)

distribution_chisquared(n, df, ncp = 0, random = FALSE, ...)

distribution_chisq(n, df, ncp = 0, random = FALSE, ...)

distribution_gamma(n, shape, scale = 1, random = FALSE, ...)

distribution_mixture_normal(n, mean = c(-3, 3), sd = 1, random = FALSE, ...)

distribution_normal(n, mean = 0, sd = 1, random = FALSE, ...)

distribution_gaussian(n, mean = 0, sd = 1, random = FALSE, ...)

distribution_nbinom(n, size, prob, mu, phi, random = FALSE, ...)

distribution_poisson(n, lambda = 1, random = FALSE, ...)

distribution_student(n, df, ncp, random = FALSE, ...)

distribution_t(n, df, ncp, random = FALSE, ...)

distribution_student_t(n, df, ncp, random = FALSE, ...)

distribution_tweedie(n, xi = NULL, mu, phi, power = NULL, random = FALSE, ...)

distribution_uniform(n, min = 0, max = 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
`random`	Generate near-perfect or random (simple wrappers for the base R `r*` functions) distributions.
`shape1`	non-negative parameters of the Beta distribution.
`shape2`	non-negative parameters of the Beta distribution.
`ncp`	non-centrality parameter.
`size`	number of trials (zero or more).
`prob`	probability of success on each trial.
`location`	location and scale parameters.
`scale`	location and scale parameters.
`df`	degrees of freedom (non-negative, but can be non-integer).
`shape`	shape and scale parameters. Must be positive, `scale` strictly.
`mean`	vector of means.
`sd`	vector of standard deviations.
`mu`	the mean
`phi`	Corresponding to `glmmTMB`'s implementation of nbinom distribution, where `size=mu/phi`.
`lambda`	vector of (non-negative) means.
`xi`	the value of xi such that the variance is var(Y) = phi mu^xi*
`power`	a synonym for xi
`min`	lower and upper limits of the distribution. Must be finite.
`max`	lower and upper limits of the distribution. Must be finite.

Examples

library(bayestestR)
x <- distribution(n = 10)
plot(density(x))

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

bayestestR

Understand and Describe Bayesian Models and Posterior Distributions

v0.10.0

GPL-3

Authors

Dominique Makowski [aut, cre] (<https://orcid.org/0000-0001-5375-9967>, @Dom_Makowski), Daniel Lüdecke [aut] (<https://orcid.org/0000-0002-8895-3206>, @strengejacke), Mattan S. Ben-Shachar [aut] (<https://orcid.org/0000-0002-4287-4801>, @mattansb), Indrajeet Patil [aut] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets), Michael D. Wilson [aut] (<https://orcid.org/0000-0003-4143-7308>), Paul-Christian Bürkner [rev], Tristan Mahr [rev] (<https://orcid.org/0000-0002-8890-5116>), Henrik Singmann [ctb] (<https://orcid.org/0000-0002-4842-3657>), Quentin F. Gronau [ctb] (<https://orcid.org/0000-0001-5510-6943>), Sam Crawley [ctb] (<https://orcid.org/0000-0002-7847-0411>)

Initial release

distribution

Description

Usage

Arguments

Examples

bayestestR

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