tdigest: Create a new t-Digest histogram from a vector

In tdigest: Wicked Fast, Accurate Quantiles Using t-Digests

Create a new t-Digest histogram from a vector

Description

The t-Digest construction algorithm, by Dunning et al., 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.

Usage

tdigest(vec, compression = 100)

## S3 method for class 'tdigest'
print(x, ...)

Arguments

vec	vector (will be converted to double if not already double). NOTE that this is ALTREP-aware and will not materialize the passed-in object in order to add the values to the t-Digest.
compression	the input compression value; should be >= 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.
x	tdigest object
...	unused

Value

a tdigest object

References

Computing Extremely Accurate Quantiles Using t-Digests

Examples

set.seed(1492)
x <- sample(0:100, 1000000, replace = TRUE)
td <- tdigest(x, 1000)
tquantile(td, c(0, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99, 1))
quantile(td)

tdigest documentation built on Oct. 5, 2022, 1:07 a.m.