HyperparametersSingleBatch {CNPBayes} | R Documentation

## Create an object of class 'HyperparametersSingleBatch' for the single batch mixture model

### Description

Create an object of class 'HyperparametersSingleBatch' for the single batch mixture model

### Usage

    HyperparametersSingleBatch(k = 0L, mu.0 = 0, tau2.0 = 0.4,
      eta.0 = 32, m2.0 = 0.5, alpha, beta = 0.1, a = 1.8, b = 6,
      dfr = 100)

### Arguments

`k` | length-one integer vector specifying number of components (typically 1 <= k <= 4)
`mu.0` | length-one numeric vector of the mean for the normal prior of the component means
`tau2.0` | length-one numeric vector of the variance for the normal prior of the component means
`eta.0` | length-one numeric vector of the shape parameter for the Inverse Gamma prior of the component variances. The shape parameter is parameterized as 1/2 * eta.0.
`m2.0` | length-one numeric vector of the rate parameter for the Inverse Gamma prior of the component variances. The rate parameter is parameterized as 1/2 * eta.0 * m2.0.
`alpha` | length-k numeric vector of the shape parameters for the dirichlet prior on the mixture probabilities
`beta` | length-one numeric vector for the parameter of the geometric prior for nu.0 (nu.0 is the shape parameter of the Inverse Gamma sampling distribution for the component-specific variances). beta is a probability and must be in the interval [0,1].
`a` | length-one numeric vector of the shape parameter for the Gamma prior used for sigma2.0 (sigma2.0 is the shape parameter of the Inverse Gamma sampling distribution for the component-specific variances)
`b` | a length-one numeric vector of the rate parameter for the Gamma prior used for sigma2.0 (sigma2.0 is the rate parameter of the Inverse Gamma sampling distribution for the component-specific variances)
`dfr` | length-one numeric vector for t-distribution degrees of freedom

### Value

An object of class HyperparametersSingleBatch

### Examples

    HyperparametersSingleBatch(k=3)

[Package CNPBayes version 1.12.0 Index]