c.bma: Concatenate bma objects

In BMS: Bayesian Model Averaging Library

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 class '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))

BMS documentation built on Aug. 9, 2022, 5:08 p.m.