pmpmodel {BMS} | R Documentation |

## Posterior Model Probability for any Model

### Description

Returns the posterior model probability for any model based on bma results

### Usage

```
pmpmodel(bmao, model = numeric(0), exact = TRUE)
```

### Arguments

`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 |

### Details

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,

### Value

A scalar with (an estimate of) the posterior model probability for
`model`

### See Also

`pmp.bma`

for similar
functions

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

### Examples

```
## 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)
```

*BMS*version 0.3.5 Index]