Predicting and calculating sequential design and optimization statistics at new design points (i.e., active learning heuristics) for dynamic tree models

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`object` |
a |

`XX` |
a design |

`yy` |
an optional vector of “true” responses at the |

`quants` |
a scalar |

`ei` |
a scalar |

`verb` |
a positive scalar integer indicating how many predictive locations
(iterations) after which a progress statement should be
printed to the console; a (default) value of |

`...` |
to comply with the generic |

`predict`

returns predictive summary statistics by averaging over the
samples from the posterior predictive distribution obtained
from each of the particles in the cloud pointed to by the
object (`object`

)

coef returns a matrix of regression coefficients used in linear
model leaves (`model = "linear"`

) leaves, averaged over all particles,
for each `XX`

location. For other models it prints a warning and
defaults to `predict`

.

The value(s) calculated are appended to `object`

; the new
fields are described below

Note that ALC calculations have been moved to the `alc.dynaTree`

function(s)

The object returned is of class `"dynaTree"`

, which includes a
copy of the list elements from the `object`

passed in,
with the following (predictive)
additions depending on whether `object$model`

is for
regression (`"constant"`

or `"linear"`

) or classification
(`"class"`

).

For regression:

`mean ` |
a vector containing an estimate of the predictive mean
at the |

`vmean ` |
a vector containing an estimate of the variance of predictive mean
at the |

`var ` |
a vector containing an estimate of the predictive
variance (average variance plus variance of mean) at the |

`df ` |
a vector containing the average degrees of freedom at the |

`q1 ` |
a vector containing an estimate of the 5% quantile of
the predictive distribution at the |

`q2 ` |
a vector containing an estimate of the 95% quantile of
the predictive distribution at the |

`yypred ` |
if |

`ei ` |
a vector containing an estimate of the EI statistic,
unless |

;

For classification:

`p ` |
a |

`entropy ` |
a |

;

For `coef`

a new **R**XXc field is created so as not to trample
on `XX`

s that may have been used in a previous `predict`

,
plus

`coef ` |
a |

matrix of particle- averaged regression coefficients.

Robert B. Gramacy rbgramacy@chicagobooth.edu,

Matt Taddy taddy@chicagobooth.edu, and
Christoforos Anagnostopoulos christoforos.anagnostopoulos06@imperial.ac.uk

Taddy, M.A., Gramacy, R.B., and Polson, N. (2011). “Dynamic trees for learning and design” Journal of the American Statistical Association, 106(493), pp. 109-123; arXiv:0912.1586

http://bobby.gramacy.com/r_packages/dynaTree/

`dynaTree`

, `update.dynaTree`

,
`plot.dynaTree`

, `alc.dynaTree`

,
`entropyX.dynaTree`

1 2 | ```
## see the example(s) section(s) of dynaTree and
## update.dynaTree and the demos (demo(package=dynaTree))
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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