HyperparametersMultiBatch: Create an object of class 'HyperparametersMultiBatch' for the...
In CNPBayes: Bayesian mixture models for copy number polymorphisms

Description Usage Arguments Value See Also Examples

Create an object of class 'HyperparametersMultiBatch' for the batch mixture model

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HyperparametersMultiBatch(k = 3L, 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)

`k`	length-one integer vector specifying number of components (typically 1 <= k <= 4)
`mu.0`	length-one numeric vector of the of the normal prior for 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, tau2_h. The shape parameter is parameterized as 1/2 * eta.0. In the batch model, tau2_h describes the inter-batch heterogeneity of means for component h.
`m2.0`	length-one numeric vector of the rate parameter for the Inverse Gamma prior of the component variances, tau2_h. The rate parameter is parameterized as 1/2 * eta.0 * m2.0. In the batch model, tau2_h describes the inter-batch heterogeneity of means for component h.
`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. Together, nu.0 and sigma2.0 model inter-component heterogeneity in 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