td_total_count

length.tdigest

Total items contained in the t-Digest

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_total_count function

<dl><dt>td</dt>
<dd>t-Digest object</dd>
<dt>x</dt>
<dd>a tdigest object</dd></dl>

Arguments

Total items contained in the t-Digest — td_total_count

<dl>

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


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

</dl>

td_total_count: Total items contained in the t-Digest

Description

Usage

Value

Arguments

Examples