bhpm.npm {bhpm} | R Documentation |

Implementation of a Bayesian Hierarchical for Grouped Data with Clusters and without Point-Mass.

bhpm.npm(cluster.data, hier = 3, sim_type = "SLICE", burnin = 10000, iter = 40000, nchains = 3, global.sim.params = NULL, sim.params = NULL, monitor = NULL, initial_values = NULL, level = 1, hyper_params = NULL, memory_model = "HIGH")

`cluster.data` |
A file or data frame containing the cluster data. It must contain the columns |

`hier` |
Fit a 2 or 3 level hierarcical model. |

`sim_type` |
The type of MCMC method to use for simulating from non-standard distributions. Allowed values are |

`burnin` |
The burnin period for the monte-carlo simulation. These are discarded from the returned samples. |

`iter` |
The total number of iterations for which the monte-carlo simulation is run. This includes the burnin period.
The total number of samples returned is |

`nchains` |
The number of independent chains to run. |

`global.sim.params` |
A data frame containing the parameters for the simuation type |

`sim.params` |
A dataframe containing simulation parameters which override the global simulation parameters ( The function |

`monitor` |
A dataframe indicating which sets of variables to monitor. Passing NULL uses the model defaults. |

`initial_values` |
The initial values for starting the chains. If NULL (the default) is passed the function generates the initial values for the chains. initial_values is a list with the following format: list(gamma, theta, mu.gamma, mu.theta, sigma2.gamma, sigma2.theta, mu.gamma.0, mu.theta.0, tau2.gamma.0, tau2.theta.0) where each element of the list is either a dataframe or array.
The function |

`level` |
The level of dependancy between the clusters. 0 - independent clusters, 1 - common cluster means, 2 - weak dependancy between clusters. |

`hyper_params` |
The hyperparameters for the model. Passing NULL uses the model defaults. |

`memory_model` |
Allowed values are "HIGH" and "LOW". "HIGH" means use as much memory as possible. "LOW" means use the minimum amount of memory. |

The model is fitted by a Gibbs sampler.
The posterior distributions for *gamma* and *theta* are sampled with either a Metropolis-Hastings step or a slice sampler.

The output from the simulation including all the sampled values is as follows:

list(id, sim_type, chains, nClusters, Clusters, nOutcome.Grp, maxOutcome.Grps, maxOutcomes, nOutcome, Outcome, Outcome.Grp, burnin, iter, monitor, mu.gamma.0, mu.theta.0, tau2.gamma.0, tau2.theta.0, mu.gamma, mu.theta, sigma2.gamma, sigma2.theta, gamma, theta, gamma_acc, theta_acc)

where

*id* - a string identifying the verion of the function

*sim_type* - an string identifying the samlping method used for non-standard distributions, either *"MH"* or *"SLICE"*

*chains* - the number of chains for which the simulation was run.

*nClusters* - the number of clusters in the simulation

*Clusters* - an array. The clusters.

*nOutcome.Grp* - the number of outcome groupings.

*maxOutcome.Grps* - the maximum number of outcome groupings in a cluster.

*maxOutcomes* - the maximum number of outcomes in an outcome grouping.

*nOutcome* - an array. The number of outcomes in each outcome grouping.

*Outcome* - an array of dimension *nOutcome.Grp*, *maxOutcomes*. The outcomes.

*Outcome.Grp* - an array. The outcome groupings.

*burnin* - burnin used for the simulation.

*iter* - the total number of iterations in the simulation.

*monitor* - the variables being monitored. A dataframe.

*mu.gamma.0* - array of generated samples.

*mu.theta.0* - array of generated samples.

*tau2.gamma.0* - array of generated samples.

*tau2.theta.0* - array of generated samples.

*mu.gamma* - array of generated samples.

*mu.theta* - array of generated samples.

*sigma2.gamma* - array of generated samples.

*sigma2.theta* - array of generated samples.

*gamma* - array of generated samples.

*theta* - array of generated samples.

*gamma_acc* - the acceptance rate for the gamma samples if a Metropolis-Hastings method is used.

*theta_acc* - the acceptance rate for the theta samples if a Metropolis-Hastings method is used.

The function performs the simulation and returns the raw output. No checks for convergence are performed.

R. Carragher

data(bhpm.cluster.data1) raw = bhpm.npm(cluster.data = bhpm.cluster.data1, burnin = 100, iter = 200) data(bhpm.cluster.data1) raw = bhpm.npm(cluster.data = bhpm.cluster.data1, level = 1)

[Package *bhpm* version 1.7 Index]