scagnostics: Calculate scagnostics for a scatterplot

In binostics: Compute Scagnostics

Description

Scagnostics summarize potentially interesting patterns in 2d scatterplot

Usage

scagnostics(x, ...)

## Default S3 method:
scagnostics(x, y, bins = 50, outlierRmv = TRUE, ...)

## S3 method for class 'matrix'
scagnostics(x, ...)

## S3 method for class 'data.frame'
scagnostics(x, ...)

scagnostics_2d(x, ...)

Arguments

x, y	object to calculate scagnostics on: a vector, a matrix or a data.frame
...	Extra arguments
bins	number of bins, default=50
outlierRmv	logical for trimming data, default=TRUE

Details

Current scagnostics are:

• Outlying

• Skewed

• Clumpy

• Sparse

• Striated

• Convex

• Skinny

• Stringy

• Monotonic

These are described in more detail in: Graph-Theoretic Scagnostics, Leland Wilkinson, Anushka Anand, Robert Grossman. https://papers.rgrossman.com/proc-094.pdf

You can call the function with two 1d vectors to get a single vector of scagnostics, or with a 2d structure (matrix or data frame) to get scagnostics for every combination of the variables.

Examples

scagnostics(1:10, 1:10)
scagnostics(rnorm(100), rnorm(100))
scagnostics(mtcars)
scagnostics(as.matrix(mtcars))

binostics documentation built on Jan. 27, 2021, 5:08 p.m.