skipTrack.diagnostics: skipTrack MCMC Diagnostics

In skipTrack: A Bayesian Hierarchical Model that Controls for Non-Adherence in Mobile Menstrual Cycle Tracking

skipTrack MCMC Diagnostics

Description

Takes model results from skipTrack.fit and uses genMCMCDiag to get generalized mcmc diagnostics

Usage

skipTrack.diagnostics(
  stFit,
  param = c("rho", "phi", "Betas", "Gammas", "muis", "tauis", "cijs"),
  proximityMap = NULL,
  ...
)

Arguments

stFit	A list of MCMC results from the skipTrack.fit function.
param	A character string specifying the parameter for which diagnostics are to be calculated. Must be one of: 'rho', 'phi', 'Betas', 'Gammas', 'muis', 'tauis', or 'cijs'.
proximityMap	An optional parameter specifying the proximity-map for calculating diagnostics. See package genMCMCDiag for details. Default is NULL.
...	Arguments passed on to genMCMCDiag::genDiagnostic diagnostics A character vector or list of diagnostic functions to be evaluated. Options include 'traceplot', 'ess', 'psrf', or custom functions. See details. distance Function for evaluating distance between MCMC draws if required by 'method'. This should be a pairwise distance function that operates on elements of the chains from mhDraws. Note that the lanfear and ts proximityMaps ALWAYS require a distance function. verbose If TRUE, informative messages are displayed.

Details

If the parameter is 'rho' or 'phi' (the univariate parameters), the function extracts the specified parameter from the MCMC results and calculates diagnostics using the genDiagnostic function with the standard proximityMap. If the parameter is any of the other available options, the function extracts the corresponding values and calculates diagnostics using the genDiagnostic function with the specified or default proximityMap ('lanfear') and hammingDist as the distance function.

Details on genDiagnostic can be found in the genMCMCDiag package.

Value

A mcmcDiag object of MCMC diagnostics for the specified parameter

See Also

genDiagnostic, skipTrack.fit

Examples

#Simulated data
simDat <- skipTrack.simulate(n = 100, skipProb = c(.7, .2, .1))

#Run model fit (should typically run with much more than 50 reps)
modFit <- skipTrack.fit(Y = simDat$Y, cluster = simDat$cluster, chains = 2, reps = 50)

#Get diagnostics for cijs
skipTrack.diagnostics(modFit, 'cijs')

skipTrack documentation built on April 3, 2025, 6:21 p.m.