bhpm.cluster.BB.hier3: A Three-Level Hierarchical Model for grouped data and...

In bhpm: Bayesian Hierarchical Poisson Models for Multiple Grouped Outcomes with Clustering

Description

Implementation of a Three-Level Hierarchical for grouped data and clusters with Point-Mass.

Usage

	bhpm.cluster.BB.hier3(cluster.data, sim_type = "SLICE", burnin = 20000,
	iter = 60000, nchains = 5, theta_algorithm = "MH",
	global.sim.params = data.frame(type = c("MH", "MH", "MH", "MH",
	"SLICE", "SLICE", "SLICE"),
	param = c("sigma_MH_alpha", "sigma_MH_beta", "sigma_MH_gamma",
	"sigma_MH_theta", "w_alpha", "w_beta", "w_gamma"),
	value = c(3, 3, 0.2, 0.25, 1, 1, 1), control = c(0, 0, 0, 0, 6, 6, 6),
	stringsAsFactors = FALSE),
	sim.params = NULL,
	monitor = data.frame(variable = c("theta", "gamma", "mu.gamma", "mu.theta",
	"sigma2.theta", "sigma2.gamma",
	"mu.theta.0", "mu.gamma.0", "tau2.theta.0", "tau2.gamma.0",
	"pi", "alpha.pi", "beta.pi"),
	monitor = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
	stringsAsFactors = FALSE),
	initial_values = NULL, level = 1, hyper_params = list(mu.gamma.0.0 = 0,
	tau2.gamma.0.0 = 10, mu.theta.0.0 = 0, tau2.theta.0.0 = 10,
	alpha.gamma.0.0 = 3, beta.gamma.0.0 = 1, alpha.theta.0.0 = 3,
	beta.theta.0.0 = 1, alpha.gamma = 3,
	beta.gamma = 1, alpha.theta = 3, beta.theta = 1,
	lambda.alpha = 1.0, lambda.beta = 1.0),
	global.pm.weight = 0.5,
	pm.weights = NULL,
	adapt_phase=1, memory_model = "HIGH")

Arguments

cluster.data	A file or data frame containing the cluster data. It must contain the columns Cluster, Outcome.Grp, Outcome, Trt.Grp (1 - control, 2,... comparator treatments), Count (total number of events), Exposure (total exposure of time of all patients fot the Trt.Grp in the Cluster).
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 iter - burnin
nchains	The number of independent chains to run.
theta_algorithm	MCMC algorithm used to sample the theta variables. "MH" is the only currently supported stable algorithm.
sim_type	The type of MCMC method to use for simulating from non-standard distributions apart from theta. Allowed values are "MH" and "SLICE" for Metropis_Hastings and Slice sampling respectively.
monitor	A dataframe indicating which sets of variables to monitor.
global.sim.params	A data frame containing the parameters for the simuation type sim_type. For "MH" the parameter is the variance of the normal distribution used to simulate the next candidate value centred on the current value. For "SLICE" the parameters are the estimated width of the slice and a value limiting the search for the next sample.
sim.params	A dataframe containing simulation parameters which override the global simulation parameters (global.sim.params) for particular model parameters. sim.params must contain the following columns: type: the simulation type ("MH" or "SLICE"); variable: the model parameter for which the simulation parameters are being overridden; Outcome.Grp (if applicable); Outcome (if applicable); param: the simulation parameter; value: the overridden value; control: the overridden control value. The function bhpm.sim.control.params generates a template for sim.params which can be edited by the user.
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, pi, mu.gamma.0, mu.theta.0, tau2.gamma.0, tau2.theta.0, alpha.pi, beta.pi) The function bhpm.gen.initial.values can be used to generate a template for the list which can be updated by the user if required.
level	Allowed valus are 0, 1, 2. Respectively these indicate independent clusters, common means across the clusters and weak relationships between the clusters.
hyper_params	The hyperparameters for the model.
global.pm.weight	A global weighting for the proposal distribution used to sample theta.
pm.weights	Override global.pm.weight for specific outcomes.
adapt_phase	Unused parameter.
memory_model	Allowed values are "HIGH" and "LOW". "HIGH" means use as much memory as possible. "LOW" means use the minimum amount of memory.

Details

The model is fitted by a Gibbs sampler.

Value

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

list(id, theta_alg, sim_type, chains, nClusters, Clusters, nOutcome.Grp,
	maxOutcome.Grps, maxOutcomes, nAE, AE, B, burnin,
	iter, monitor, mu.gamma.0, mu.theta.0, tau2.gamma.0, tau2.theta.0,
	mu.gamma, mu.theta, sigma2.gamma, sigma2.theta, pi, alpha.pi, beta.pi,
	alpha.pi_acc, beta.pi_acc, gamma, theta, gamma_acc, theta_acc)

where

id - a string identifying the verions of the function.

theta_alg - an string identifying the algorithm used to smaple theta.

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 outcome in a 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.

pi - array of generated samples. alpha.pi - array of generated samples. beta.pi - array of generated samples.

alpha.pi_acc - the acceptance rate for the alpha.pi samples if a Metropolis-Hastings method is used.

beta.pi_acc - the acceptance rate for the beta.pi samples if a Metropolis-Hastings method is used.

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.

Note

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

Author(s)

R. Carragher

Examples

data(bhpm.cluster.data1)
raw = bhpm.cluster.BB.hier3(bhpm.cluster.data1, level = 1, , burnin = 100, iter = 200)


data(bhpm.cluster.data1)
raw = bhpm.cluster.BB.hier3(bhpm.cluster.data1, level = 1)

bhpm documentation built on April 1, 2020, 5:09 p.m.