# distribution: Empirical Distributions In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

## Description

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

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39``` ```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

 ```1 2 3 4 5 6``` ```library(bayestestR) x <- distribution(n = 10) plot(density(x)) x <- distribution(type = "gamma", n = 100, shape = 2) plot(density(x)) ```

bayestestR documentation built on July 26, 2021, 5:08 p.m.