Generate a sequence of n-quantiles, i.e., a sample of size n
with a
near-perfect distribution.
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)
Can be any of the names from base R's
Distributions, like "cauchy"
, "pois"
or
"beta"
.
Arguments passed to or from other methods.
the number of observations
Generate near-perfect or random (simple wrappers for the base R
r*
functions) distributions.
non-negative parameters of the Beta distribution.
non-centrality parameter.
number of trials (zero or more).
probability of success on each trial.
location and scale parameters.
degrees of freedom (non-negative, but can be non-integer).
Shape parameter.
vector of means.
vector of standard deviations.
the mean
Corresponding to glmmTMB
's implementation of nbinom
distribution, where size=mu/phi
.
vector of (non-negative) means.
For tweedie distributions, the value of xi
such that the variance
is var(Y) = phi * mu^xi
.
Alias for xi
.
lower and upper limits of the distribution. Must be finite.
When random = FALSE
, these function return q*(ppoints(n), ...)
.
library(bayestestR)
x <- distribution(n = 10)
plot(density(x))
x <- distribution(type = "gamma", n = 100, shape = 2)
plot(density(x))
Run the code above in your browser using DataLab