dirichlet.model.options: dirichlet.model.options

In kreidles/informativeDropout: informativeDropout: varying coefficient models to account for informative dropout

Description

Model options for the Dirichlet Process model. Includes starting values, priors and simulation/MCMC parameters.

Usage

dirichlet.model.options(iterations = 10000, burnin = 500, thin = 1,
  print = 1, start.weights.mixing = NULL, n.clusters = 15,
  dropout.estimationTimes = NULL,
  dropout.estimationTimes.censored = NULL, dropout.offset = 0,
  dp.concentration = NULL, dp.concentration.alpha = NULL,
  dp.concentration.beta = NULL, dp.cluster.sigma = NULL,
  dp.cluster.sigma.nu0 = NULL, dp.cluster.sigma.T0 = NULL,
  dp.dist.mu0 = NULL, dp.dist.mu0.mb = NULL, dp.dist.mu0.Sb = NULL,
  dp.dist.sigma0 = NULL, dp.dist.sigma0.nub = NULL,
  dp.dist.sigma0.Tb = NULL, betas.covariates = NULL,
  betas.covariates.mu = NULL, betas.covariates.sigma = NULL,
  sigma.error = NULL, sigma.error.tau = NULL,
  density.intercept.domain = NULL, density.slope.domain = NULL,
  censoring.var = NULL)

Arguments

iterations	number of iterations for the MCMC simulation
burnin	burn in period for the simulation, i.e. the number of iterations to throw away at the beginning of the simulation
thin	thinning interval, i.e. if thin=n, only keep every nth iteration
print	printing interval, i.e. if print=n, print a counter on every nth iteration
dp.concentration	prior value for the concentration parameter of the Dirichlet process
dp.concentration.alpha	Shape parameter for the hyperprior Gamma distribution of the concentration parameter of the Dirichlet process
dp.concentration.beta	Rate parameter for the hyperprior Gamma distribution of the concentration parameter of the Dirichlet process
dp.cluster.sigma	prior for the common cluster covariance
dp.cluster.sigma.nu0	Degrees of freedom for the hyperprior inverse Wishart distribution of the common cluster covariance
dp.cluster.sigma.T0	Scale matrix for the hyperprior inverse Wishart distribution of the common cluster covariance
dp.dist.mu0	prior mean for the baseline distribution of the Dirichlet process
dp.dist.mu0.mb	Mean for the hyperprior Gaussian distribution of the mean of the baseline distribution of the Dirichlet process
dp.dist.mu0.Sb	Covariance for the hyperprior Gaussian distribution of the mean of the baseline distribution of the Dirichlet process
dp.dist.sigma0	prior covariance for the baseline distribution of the Dirichlet process
dp.dist.sigma0.nub	Degrees of freedom for the hyperprior inverse Wishart distribution of the covariance of the baseline distribution of the Dirichlet process
dp.dist.sigma0.Tb	Scale matrix for the hyperprior inverse Wishart distribution of the covariance of the baseline distribution of the Dirichlet process
betas.covariates	prior value for covariate regression coefficients
betas.covariates.mu	Prior mean for the covariate regression coefficients
betas.covariates.sigma	Prior covariance for the covariate regression coefficients
sigma.error	Prior for the residual error (Gaussian outcomes only)
sigma.error.tau	Hyperprior for the residual error (Gaussian outcomes only)
density.intercept.domain	vector of values at which to calculate the density of the random intercepts. If NULL, no density will be calculated
density.slope.domain	vector of values at which to calculate the density of the random slopes. If NULL, no density will be calculated
censoring.var	column of data set containing an indicator for administrative censoring (optional). 1=dropout time censored, 0=actual dropout time observed.
numClusters	number of clusters for the Dirichlet Process stick breaking model

kreidles/informativeDropout documentation built on Sept. 13, 2020, 12:15 a.m.