td_create

is_tdigest

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

td_create function

<dl><dt>compression</dt>
<dd>the input compression value; should be &gt;= 1.0; this
will control how aggressively the t-Digest compresses data together.
The original t-Digest paper suggests using a value of 100 for a good
balance between precision and efficiency. It will land at very small
(think like 1e-6 percentile points) errors at extreme points in the
distribution, and compression ratios of around 500 for large data sets
(~1 million datapoints). Defaults to 100.</dd>
<dt>td</dt>
<dd>t-digest object</dd></dl>

Arguments

Allocate a new histogram — td_create

<dl>

<dt>compression</dt>
<dd>the input compression value; should be &gt;= 1.0; this
will control how aggressively the t-Digest compresses data together.
The original t-Digest paper suggests using a value of 100 for a good
balance between precision and efficiency. It will land at very small
(think like 1e-6 percentile points) errors at extreme points in the
distribution, and compression ratios of around 500 for large data sets
(~1 million datapoints). Defaults to 100.</dd>


<dt>td</dt>
<dd>t-digest object</dd>

</dl>

td_create: Allocate a new histogram

Description

Usage

Value

Arguments

References

Examples