BMS (version 0.3.5)

c.bma: Concatenate bma objects

Description

Combines bma objects (resulting from bms). Can be used to split estimation over several machines, or combine the MCMC results obtained from different starting points.

Usage

# S3 method for bma
c(..., recursive = FALSE)

Arguments

...

At least two 'bma' objects (cf. bms)

recursive

retained for compatibility with c method

Details

Aggregates the information obtained from several chains. The result is a 'bma' object (cf. 'Values' in bms) that can be used just as a standard 'bma' object.
Note that combine_chains helps in particular to paralllelize the enumeration of the total model space: A model with \(K\) regressors has \(2^K\) potential covariate combinations: With \(K\) large (more than 25), this can be pretty time intensive. With the bms arguments start.value and iter, sampling can be done in steps: cf. example 'enumeration' below.

See Also

bms for creating bma objects

Check http://bms.zeugner.eu for additional help.

Examples

 data(datafls)
  
 #MCMC case ############################
 model1=bms(datafls,burn=1000,iter=4000,mcmc="bd",start.value=c(20,30,35))
 model2=bms(datafls,burn=1500,iter=7000,mcmc="bd",start.value=c(1,10,15))
 
 model_all=c(model1,model2)
 coef(model_all)
 plot(model_all)
 
 
 
 #splitting enumeration ########################
 
 #standard case with 12 covariates (4096 differnt combinations):
 enum0=bms(datafls[,1:13],mcmc="enumerate")
 
 # now split the task:
 # enum1 does everything from model zero (the first model) to model 1999
 enum1=bms(datafls[,1:13],mcmc="enumerate",start.value=0,iter=1999)
 
 # enum2 does models from index 2000 to the index 3000 (in total 1001 models)
 enum2=bms(datafls[,1:13],mcmc="enumerate",start.value=2000,iter=1000)
 
 # enum3 does models from index 3001 to the end
 enum3=bms(datafls[,1:13],mcmc="enumerate",start.value=3001)
 
 enum_combi=c(enum1,enum2,enum3)
 coef(enum_combi)
 coef(enum0)
 #both enum_combi and enum0 have exactly the same results 
 #(one difference: enum_combi has more 'top models' (1500 instead of 500))