print.topmod {BMS} | R Documentation |

## Printing topmod Objects

### Description

Print method for objects of class 'topmod', typically the best models stored in a 'bma' object

### Usage

```
## S3 method for class 'topmod'
print(x, ...)
```

### Arguments

`x` |
an object of class 'topmod' - see |

`...` |
additional arguments passed to |

### Details

See `pmp.bma`

for an explanation of likelihood vs. MCMC
frequency concepts

### Value

if `x`

contains more than one model, then the function returns
a 2-column matrix:

`Row Names` |
show the model binaries in hexcode |

`Column 'Marg.Log.Lik'` |
shows the
marginal log-likelihoods of the models in |

```
Column 'MCMC
Freq'
``` |
shows the MCMC frequencies of the models in |

if `x`

contains only one model, then more detailed information is shown
for this model:

`first line` |
'Model Index' provides the model binary in
hexcode, 'Marg.Log.Lik' its marginal log likelhood, 'Sampled Freq.' how
often it was accepted (function |

`Estimates` |
first column: covariate indices included in the model,
second column: posterior expected value of the coefficients, third column:
their posterior standard deviations (excluded if no coefficients were stored
in the topmod object - cf. argument |

`Included Covariates` |
the model binary |

```
Additional
Statistics
``` |
any custom additional statistics saved with the model |

### See Also

`topmod`

for creating topmod objects, `bms`

for their typical use, `pmp.bma`

for comparing posterior model
probabilities

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

### Examples

```
# do some small-scale BMA for demonstration
data(datafls)
mm=bms(datafls[,1:10],nmodel=20)
#print info on the best 20 models
print(mm$topmod)
print(mm$topmod,digits=10)
#equivalent:
cbind(mm$topmod$lik(),mm$topmod$ncount())
#now print info only for the second-best model:
print(mm$topmod[2])
#compare 'Included Covariates' to:
topmodels.bma(mm[2])
#and to
as.vector(mm$topmod[2]$bool_binary())
```

*BMS*version 0.3.5 Index]