| c.bma {BMS} | R Documentation |
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.
## S3 method for class 'bma' c(..., recursive = FALSE)
... |
At least two 'bma' objects (cf. |
recursive |
retained for compatibility with |
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.
bms for creating bma objects
Check http://bms.zeugner.eu for additional help.
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))