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