Allows the removal (or “retireing”
of `X`

-`y`

pairs from a
`"dynaTree"`

-class object to facilitate online
learning; “retireed” pairs ar absorbed into
the leaf prior(s)

1 2 | ```
## S3 method for class 'dynaTree'
retire(object, indices, lambda = 1, verb = 0)
``` |

`object` |
a |

`indices` |
a vector of positive integers in |

`lambda` |
a scalar proportion (forgetting factor) used to downweight the previous prior summary statistics |

`verb` |
a nonzero scalar causes info about the “retireed” indices,
i.e., their |

Primarily for use in online learning contexts. After
“retireing” the predictive distribution remains unchanged,
because the sufficient statistics of the removed pairs enters
the prior in the leaves of the tree of each particle. Further
`update.dynaTree`

calls (adding data) may cause
changes to the posterior predictive as grow moves cannot keep
the “retires”; see a forthcoming paper for more
details. In many ways, `retire.dynaTree`

is the
opposite of `update.dynaTree`

except that the loss of
information upon “retireing” is not complete.

Drifting regression or classification relationships may be modeled
with a forgetting factor `lambda < 1`

The `alcX.dynaTree`

provides a good, and computationally
efficient, heuristic for choosing which points to “retire” for
regression models, and likewise `link{entropyX.dynaTree}`

for
classification models.

Note that classification models (`model = "class"`

) are
not supported, and implicit intercepts (`icept = "implicit"`

)
with linear models (`model = "linear"`

) are not supported
at this time

returns a `"dynaTree"`

-class object with updated attributes

In order to use `model = "linear"`

with
`dynaTree`

and retirement one must also specify
`icept = "augmented"`

which automatically augments an
extra column of ones onto the input `X`

design matrix/matrices.
The `retire`

function only supports this `icept`

case

Robert B. Gramacy rbgramacy@chicagobooth.edu,

Matt Taddy taddy@chicagobooth.edu, and

Christoforos Anagnostopoulos christoforos.anagnostopoulos06@imperial.ac.uk

Anagnostopoulos, C., Gramacy. R.B. (2013) “Information-Theoretic Data Discarding for Dynamic Trees on Data Streams.” Entropy, 15(12), 5510-5535; arXiv:1201.5568

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

`dynaTree`

, `alcX.dynaTree`

,
`entropyX.dynaTree`

, `update.dynaTree`

,
`rejuvenate.dynaTree`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
n <- 100
Xp <- runif(n,-3,3)
XX <- seq(-3,3, length=200)
Yp <- Xp + Xp^2 + rnorm(n, 0, .2)
rect <- c(-3,3)
out <- dynaTree(Xp, Yp, model="linear", icept="augmented")
## predict and plot
out <- predict(out, XX)
plot(out, main="parabola data", lwd=2)
## randomly remove half of the data points
out <- retire(out, sample(1:n, n/2, replace=FALSE))
## predict and add to plot -- shouldn't change anything
out <- predict(out, XX)
plot(out, add=TRUE, col=3)
points(out$X[,-1], out$y, col=3)
## now illustrating rejuvenation, which should result
## in a change to the predictive surface
out <- rejuvenate(out)
out <- predict(out, XX)
plot(out, add=TRUE, col=4)
legend("top", c("original", "retired", "rejuvenated"),
col=2:4, lty=1)
## clean up
deletecloud(out)
## see demo("online") for an online learning example
## where ALC is used for retirement
``` |

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