NMixChainsDerived

C_NMix_ChainsDerived

an object of class <code>NMixMCMC</code> or <code>NMixMCMClist</code>

object

Currently, this function creates chains for marginal means
 of exp(data) from previously sampled values (see <code><a rd-options="" href="/link/NMixMCMC?package=mixAK&version=5.3" data-mini-rdoc="mixAK::NMixMCMC">NMixMCMC</a></code>).
 This is useful in survival context when a density
 of \(Y=\log(T)\) is modelled using the function
 <code><a rd-options="" href="/link/NMixMCMC?package=mixAK&version=5.3" data-mini-rdoc="mixAK::NMixMCMC">NMixMCMC</a></code> and we are interested in inference
 on \(\mbox{E}T = \mbox{E}\exp(Y)\).

multivariate

smooth

Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.

Arnošt Komárek

mixAK

Multivariate Normal Mixture Models and Mixtures of Generalized
Linear Mixed Models Including Model Based Clustering

NMixChainsDerived function

Currently, this function creates chains for marginal means
 of exp(data) from previously sampled values (see <code><a rd-options='' href='NMixMCMC'>NMixMCMC</a></code>).
 This is useful in survival context when a density
 of \(Y=\log(T)\) is modelled using the function
 <code><a rd-options='' href='NMixMCMC'>NMixMCMC</a></code> and we are interested in inference
 on \(\mbox{E}T = \mbox{E}\exp(Y)\).

NMixChainsDerived: Create MCMC chains derived from previously sampled values

Description

Usage

Arguments

Value

See Also