bayesMCClust (version 1.0)

calcRegCoeffs: Calculates Posterior Expectations, Standard Deviations and (Optionally) HPD Intervals for the MNL Regression Coefficients

Description

Calculates posterior expectations, standard deviations and (optional) highest probability density (HPD) intervals for the multinomial logit (MNL) regression coefficients (using boa.hpd from package boa) and also offers some other analyses like plotting paths and autocorrelation functions (ACFs) for the corresponding MCMC draws.

Usage

calcRegCoeffs(outList, hBase = 1, thin = 1, M0 = outList$Mcmc$M0, 
              grLabels = paste("Group", 1:outList$Prior$H), 
              printHPD = TRUE, plotPaths = TRUE, plotACFs = TRUE)

Value

A list containing:[[h]], h=1,..,HA matrix containing posterior expectation ("Post Exp"), standard deviation ("Post Sd") and HPD interval ("HPD Lower B", "HPD Upper B") for the MNL regression coefficients in cluster/group $h$ except for the baseline cluster/group.regCoeffsAllA matrix containing posterior expectation ("Post Exp") and (in parenthesis) standard deviation ("Post Sd") for the MNL regression coefficients for all clusters/groups.

References

Sylvia Fruehwirth-Schnatter, Christoph Pamminger, Andrea Weber and Rudolf Winter-Ebmer, (2011), "Labor market entry and earnings dynamics: Bayesian inference using mixtures-of-experts Markov chain clustering". Journal of Applied Econometrics. DOI: 10.1002/jae.1249 http://onlinelibrary.wiley.com/doi/10.1002/jae.1249/abstract Christoph Pamminger and Sylvia Fruehwirth-Schnatter, (2010), "Model-based Clustering of Categorical Time Series". Bayesian Analysis, Vol. 5, No. 2, pp. 345-368. DOI: 10.1214/10-BA606 http://ba.stat.cmu.edu/journal/2010/vol05/issue02/pamminger.pdf

See Also

boa.hpd, acf, mcClustExtended, dmClustExtended, MNLAuxMix

Examples

# please run the examples in mcClustExtended, dmClustExtended and 
# MNLAuxMix