NMixMCMC or GLMM_MCMC function.
The summary also takes into account
possible scaling and shifting of the data (see argument scale
in NMixMCMC function
or argument scale.b in GLMM_MCMC). Note that even though the mixture components are re-labeled before the
summary is computed to achieve some identifiability, posterior summaries of
individual mixture means and variances are not always the quantity we
would like to see. For density estimation, posterior
predictive density (NMixPredDensMarg,
NMixPredDensJoint2) is usually the right stuff one
should be interested in.
NMixSummComp(x)## S3 method for class 'default':
NMixSummComp(x)
## S3 method for class 'NMixMCMC':
NMixSummComp(x)
## S3 method for class 'GLMM_MCMC':
NMixSummComp(x)
NMixMCMC or GLMM_MCMCx. The rest is printed on output device.NMixMCMC, GLMM_MCMC.