margModelPosterior: Marginal Model Posterior

In PReMiuM: Dirichlet Process Bayesian Clustering, Profile Regression

Marginal Model Posterior

Description

Compute the marginal model posterior.

Usage

margModelPosterior(runInfoObj,allocation)

Arguments

runInfoObj	An object of type runInfoObj.
allocation	By default, if allocation is not provided, the _z.txt file is read to compute the marginal model posterior for all the partitions available there. If allocation is equal to a vector that corresponds to a partition, the marginal model posterior is computed for that given partition.

Value

It returns a file in the output folder, with name ending in "_margModPost.txt", that contains the marginal model posterior. It also returns a list. The first argument is called margModPost and it is the mean of the values of the marginal model posterior as they appear in the file ending in "_margModPost.txt" in the output folder. The second argument is an updated runInfoObj which also include some hyperparameter values.

Authors

Silvia Liverani, Department of Epidemiology and Biostatistics, Imperial College London and MRC Biostatistics Unit, Cambridge, UK

Maintainer: Silvia Liverani <[e-mail]>

References

Silvia Liverani, David I. Hastie, Lamiae Azizi, Michail Papathomas, Sylvia Richardson (2015). PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes. Journal of Statistical Software, 64(7), 1-30. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v064.i07")}.

Examples

## Not run: 
inputs <- generateSampleDataFile(clusSummaryBernoulliDiscrete())

runInfoObj<-profRegr(yModel=inputs$yModel, 
         xModel=inputs$xModel, nSweeps=5, 
         nBurn=10, data=inputs$inputData, output="output", 
         covNames = inputs$covNames, nClusInit=15,
         fixedEffectsNames = inputs$fixedEffectNames)

margModelPost<-margModelPosterior(runInfoObj)

## End(Not run)

PReMiuM documentation built on Nov. 14, 2023, 1:08 a.m.