MixtureModel-class {CNPBayes} | R Documentation

## An object for running MCMC simulations.

### Description

BatchModel and MarginalModel both inherit from this class.

### Slots

`k`

  An integer value specifying the number of latent classes.

`hyperparams`

  An object of class 'Hyperparameters' used to specify the hyperparameters of the model.

`theta`

  the means of each component and batch

`sigma2`

  the variances of each component and batch

`nu.0`

  the shape parameter for sigma2

`sigma2.0`

  the rate parameter for sigma2

`pi`

  mixture probabilities which are assumed to be the same for all batches

`mu`

  overall mean

`tau2`

  overall variance

`data`

  the data for the simulation.

`data.mean`

  the empirical means of the components

`data.prec`

  the empirical precisions

`z`

  latent variables

`zfreq`

  table of latent variables

`probz`

  n x k matrix of probabilities

`u`

  chi-square draws for controlling t-distribution

`logprior`

  log likelihood of prior: log(p(sigma2.0)p(nu.0)p(mu))

`loglik`

  log likelihood: ∑ p_k Φ(θ_k, σ_k)

`mcmc.chains`

  an object of class 'McmcChains' to store MCMC samples

`batch`

  an integer-vector numbering the different batches. Must the same length as `data`.

`batchElements`

  a vector labeling from which batch each observation came from

`modes`

  the values of parameters from the iteration which maximizes log likelihood and log prior

`mcmc.params`

  An object of class 'McmcParams'

`label_switch`

  length-one logical indicating problems with label switching

`marginal_lik`

  the marginal likelihood of the model

`.internal.constraint`

  Constraint on parameters. For internal use only.

`.internal.counter`

  For internal use only.

`marginal_lik`

  scalar for marginal likelihood

[Package CNPBayes version 1.13.5 Index]