skipTrack.results {skipTrack} | R Documentation |
Get tables of Inference results from skipTrack.fit
Description
This function calculates inference results on Betas, Gammas, and cijs based on the provided MCMC results. It returns summaries such as credible intervals for Betas, Gammas, wald-type confidence intervals for cijs, and Gelman-Rubin diagnostics for all 3. Note that true values and converage are included in the output if trueVals is supplied, but otherwise not.
Usage
skipTrack.results(stFit, trueVals = NULL, burnIn = 750)
Arguments
stFit |
Object result of skipTrack.fit function. |
trueVals |
Optional named list containing true values for Betas, Gammas, and cijs. (Also can use output of skipTrack.simulate) |
burnIn |
Number of MCMC iterations to discard as burn-in per chain. |
Value
A list containing the following elements:
Betas |
data.frame with 95% credible intervals and (if trueVals is supplied) true values for Betas and Coverage tag. |
Gammas |
data.frame with 95% credible intervals and (if trueVals is supplied) true values for Gammas and Coverage tag. |
cijs |
data.frame with Wald-type 95% confidence intervals and (if trueVals is supplied) true values for cijs and Coverage tags. |
Diagnostics |
data.frame with ess and gelman-rubin diagnostics from genMCMCDiag package, for parameter sets 'Betas', 'Gammas' and 'cijs'. |
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)
modFit
# If using simulated data (which includes access to ground truth):
#
skipTrack.results(modFit, trueVals = simDat, burnIn = 25)
#Recommended burnIn with real data is at least 750
#
# If not using simulated data:
#
skipTrack.results(modFit, burnIn = 25)
#Recommended burnIn with real data is at least 750