adapt | R Documentation |

Update the model in adaptive mode.

```
adapt(object, n.iter, end.adaptation=FALSE, ...)
```

`object` |
a |

`n.iter` |
length of the adaptive phase |

`end.adaptation` |
logical flag. If |

`...` |
additional arguments to the update method |

This function is not normally called by the user. It is called by the
`jags.model`

function when the model object is created.

When a JAGS model is compiled, it may require an initial sampling phase during which the samplers adapt their behaviour to maximize their efficiency (e.g. a Metropolis-Hastings random walk algorithm may change its step size). The sequence of samples generated during this adaptive phase is not a Markov chain, and therefore may not be used for posterior inference on the model.

The `adapt`

function updates the model for `n.iter`

iterations in adaptive mode. Then each sampler reports whether it
has acheived optimal performance (e.g. whether the rejection rate of a
Metropolis-Hasting sampler is close to the theoretical optimum). If
any sampler reports failure of this test then `adapt`

returns
`FALSE`

.

If `end.adaptation = TRUE`

, then adaptive mode is turned off on
exit, and further calls to `adapt()`

do nothing. The model may be
maintained in adaptive mode with the default option ```
end.adaptation =
FALSE
```

so that successive calls to `adapt()`

may be made until
adaptation is satisfactory.

Returns `TRUE`

if all the samplers in the model have successfully
adapted their behaviour to optimum performance and `FALSE`

otherwise.

Martyn Plummer

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