pmpmodel {BMS} | R Documentation |
Returns the posterior model probability for any model based on bma results
pmpmodel(bmao, model = numeric(0), exact = TRUE)
bmao |
A bma object as created by |
model |
A model index - either variable names, or a logical with model binaries, or the model hexcode (cf. |
exact |
If |
If the model as provided in model
is the null or the full model, or is contained in bmao
's topmod object (cf. argument nmodel
in bms
),
then the result is the same as in pmp.bma
.
If not and exact=TRUE
, then pmpmodel
estimates the model based on comparing its marginal likelihood (times model prior) to the likelihoods in the topmod
object and multiplying by their sum of PMP according to MCMC frequencies,
A scalar with (an estimate of) the posterior model probability for model
Stefan Zeugner
pmp.bma
and topmodels.bma
for similar functions
Check http://bms.zeugner.eu for additional help.
## sample BMA for growth dataset, enumeration sampler data(datafls) mm=bms(datafls[,1:10],nmodel=5) #show the best 5 models: pmp.bma(mm) #first column: posterior model prob based on model likelihoods, #second column: posterior model prob based MCMC frequencies, ### Different ways to get the same result: ######### #PMP of 2nd-best model (hex-code representation) pmpmodel(mm,"00c") #PMP of 2nd-best model (binary representation) incls=as.logical(beta.draws.bma(mm)[,2]) pmpmodel(mm,incls) #PMP of 2nd-best model (via variable names) #names of regressors in model "00c": names(datafls[,2:10])[incls] pmpmodel(mm,c("SubSahara", "LatAmerica")) #PMP of 2nd-best model (via positions) pmpmodel(mm,c(6,7)) ####PMP of another model ######### pmpmodel(mm,1:5)