View source: R/beta_distribution.R
| beta_distribution | R Documentation |
Functions to compute the probability density function, cumulative distribution function, and quantile function for the Beta distribution.
beta_distribution(alpha, beta)
beta_pdf(x, alpha, beta)
beta_lpdf(x, alpha, beta)
beta_cdf(x, alpha, beta)
beta_lcdf(x, alpha, beta)
beta_quantile(p, alpha, beta)
beta_find_alpha(mean = NULL, variance = NULL, beta = NULL, x = NULL, p = NULL)
beta_find_beta(mean = NULL, variance = NULL, alpha = NULL, x = NULL, p = NULL)
alpha |
shape parameter (alpha > 0) |
beta |
shape parameter (beta > 0) |
x |
quantile (0 <= x <= 1) |
p |
probability (0 <= p <= 1) |
mean |
Mean of the Beta distribution |
variance |
Variance of the Beta distribution |
A single numeric value with the computed probability density, log-probability density, cumulative distribution, log-cumulative distribution, or quantile depending on the function called.
Boost Documentation for more details on the mathematical background.
# Beta distribution with shape parameters alpha = 2, beta = 5
dist <- beta_distribution(2, 5)
# Apply generic functions
cdf(dist, 0.5)
logcdf(dist, 0.5)
pdf(dist, 0.5)
logpdf(dist, 0.5)
hazard(dist, 0.5)
chf(dist, 0.5)
mean(dist)
median(dist)
mode(dist)
range(dist)
quantile(dist, 0.2)
standard_deviation(dist)
support(dist)
variance(dist)
skewness(dist)
kurtosis(dist)
kurtosis_excess(dist)
# Convenience functions
beta_pdf(0.5, 2, 5)
beta_lpdf(0.5, 2, 5)
beta_cdf(0.5, 2, 5)
beta_lcdf(0.5, 2, 5)
beta_quantile(0.5, 2, 5)
## Not run:
# Find alpha given mean and variance
beta_find_alpha(mean = 0.3, variance = 0.02)
# Find alpha given beta, x, and probability
beta_find_alpha(beta = 5, x = 0.4, p = 0.6)
# Find beta given mean and variance
beta_find_beta(mean = 0.3, variance = 0.02)
# Find beta given alpha, x, and probability
beta_find_beta(alpha = 2, x = 0.4, p = 0.6)
## End(Not run)
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