DirichletProcessBeta {dirichletprocess} | R Documentation |
Dirichlet process mixture of the Beta distribution.
Description
Create a Dirichlet process object using the mean and scale parameterisation of the Beta distribution bounded on .
Usage
DirichletProcessBeta(
y,
maxY,
g0Priors = c(2, 8),
alphaPrior = c(2, 4),
mhStep = c(1, 1),
hyperPriorParameters = c(1, 0.125),
verbose = TRUE,
mhDraws = 250
)
Arguments
y |
Data for which to be modelled. |
maxY |
End point of the data |
g0Priors |
Prior parameters of the base measure |
alphaPrior |
Prior parameters for the concentration parameter. See also |
mhStep |
Step size for Metropolis Hastings sampling algorithm. |
hyperPriorParameters |
Hyper-prior parameters for the prior distributions of the base measure parameters |
verbose |
Logical, control the level of on screen output. |
mhDraws |
Number of Metropolis-Hastings samples to perform for each cluster update. |
Details
.
The parameter also has a prior distribution
if the user selects
Fit(...,updatePrior=TRUE)
.
Value
Dirichlet process object
[Package dirichletprocess version 0.4.2 Index]