as.list.tdigest

as_tdigest

These functions make it possible to create &amp; populate a tdigest, serialize it out,
read it in at a later time and continue populating it enabling compact
distribution accumulation &amp; storage for large, "continuous" datasets.

The t-Digest construction algorithm, by
Dunning et al., (2019) <doi:10.48550/arXiv.1902.04023>, uses a variant of 1-dimensional
k-means clustering to produce a very compact data structure that allows
accurate estimation of quantiles. This t-Digest data structure can be used
to estimate quantiles, compute other rank statistics or even to estimate
related measures like trimmed means. The advantage of the t-Digest over
previous digests for this purpose is that the t-Digest handles data with
full floating point resolution. The accuracy of quantile estimates produced
by t-Digests can be orders of magnitude more accurate than those produced
by previous digest algorithms. Methods are provided to create and update
t-Digests and retrieve quantiles from the accumulated distributions.

Bob Rudis

tdigest

Wicked Fast, Accurate Quantiles Using t-Digests

Ted Dunning

Andrew Werner

as.list.tdigest function

<dl><dt>x</dt>
<dd>a tdigest object or a tdigest_list object</dd>
<dt>...</dt>
<dd>unused</dd></dl>

Arguments

Serialize a tdigest object to an R list or unserialize a serialized tdigest
list back into a tdigest object — as.list.tdigest

<dl>

<dt>x</dt>
<dd>a tdigest object or a tdigest_list object</dd>


<dt>...</dt>
<dd>unused</dd>

</dl>

as.list.tdigest: Serialize a tdigest object to an R list or unserialize a serialized tdigest list back into a tdigest object

Description

Usage

Arguments

Examples