The Beta Distribution in Regression Parameterization
Description
Density, distribution function, quantile function, and random generation for the beta distribution in regression parameterization.
Usage
dbetar(x, mu, phi, log = FALSE)
pbetar(q, mu, phi, lower.tail = TRUE, log.p = FALSE)
qbetar(p, mu, phi, lower.tail = TRUE, log.p = FALSE)
rbetar(n, mu, phi)
Arguments
x, q
|
numeric. Vector of quantiles. |
p
|
numeric. Vector of probabilities. |
n
|
numeric. Number of observations. If length(n) > 1, the length is taken to be the number required.
|
mu
|
numeric. The mean of the beta distribution. |
phi
|
numeric. The precision parameter of the beta distribution. |
log, log.p
|
logical. If TRUE, probabilities p are given as log(p). |
lower.tail
|
logical. If TRUE (default), probabilities are P[X <= x] otherwise, P[X > x]. |
Details
This is the reparameterization of the beta distribution with mean mu and precision phi, as employed in beta regression. The classic parameterization of the beta distribution is obtained by setting shape1 = mu * phi and shape2 = (1 - mu) * phi, respectively.
Value
dbetar gives the density, pbetar gives the distribution function, qbetar gives the quantile function, and rbetar generates random deviates.
See Also
dbeta, BetaR