| Title: | Compute scagnostics - scatterplot diagnostics |
|---|---|
| Description: | Calculates graph theoretic scagnostics. Scagnostics describe various measures of interest for pairs of variables, based on their appearance on a scatterplot. They are useful tool for discovering interesting or unusual scatterplots from a scatterplot matrix, without having to look at every individual plot. |
| Authors: | Lee Wilkinson <[email protected]> and Anushka Anand |
| Maintainer: | Simon Urbanek <[email protected]> |
| License: | BSD_2_clause + file LICENSE |
| Version: | 0.2-6 |
| Built: | 2024-11-17 06:32:05 UTC |
| Source: | CRAN |
Scagnostics (scatterplot diagnostics) summarize potentially interesting patterns in 2d scatterplots.
scagnostics(x, ...) scagnosticsOutliers(scagnostics) scagnosticsExemplars(scagnostics) scagnosticsGrid(scagnostics)scagnostics(x, ...) scagnosticsOutliers(scagnostics) scagnosticsExemplars(scagnostics) scagnosticsGrid(scagnostics)
x |
object to calculate scagnostics on: a vector, a matrix or a data.frame |
... |
... |
scagnostics |
objects returned from the |
scagnostics returns a vector (for a pair) or a matrix
consisting of scagnostics (rows) by variable pairs (columns).
scagnostics.outliers and scagnostics.exemplars return a
logical vector.
scagnostics.grid returns a data frame with columns x and
y which as a pair define the index of variables corresponding
to the entries in the scagnostics matrix. Hence it has as many rows as
there are columns in the scagnostics matrix and each row defines one
pair of variables.
Lee Wilkinson <[email protected]>, Anushka Anand (University of Illinois at Chicago), R package: Simon Urbanek <[email protected]>
Wilkinson L., Anand, A., and Grossman, R. (2006). High-Dimensional visual analytics: Interactive exploration guided by pairwise views of point distributions. IEEE Transactions on Visualization and Computer Graphics, November/December 2006 (Vol. 12, No. 6) pp. 1363-1372.
Wilkinson L., Anand, A., and Grossman, R. (2005). Graph-Theoretic Scagnostics. Proceedings of the 2005 IEEE Symposium on Information Visualization, p. 21.
https://www.cs.uic.edu/~wilkinson/
# tesing various forms scagnostics(1:10, 1:10) scagnostics(rnorm(100), rnorm(100)) scagnostics(as.matrix(mtcars)) # more real use on a dataset s <- scagnostics(mtcars) # look at outliers o <- scagnosticsOutliers(s) o[o] # one outlier, let's plot it g <- scagnosticsGrid(s) go <- g[o,] plot(mtcars[[go$x]], mtcars[[go$y]], pch=19, xlab=names(mtcars)[go$x], ylab=names(mtcars)[go$y]) # find and plot exemplars e <- scagnosticsExemplars(s) e[e] ge <- g[e,] par(mfrow = c(2,2)) for (i in 1:dim(ge)[1]) plot(mtcars[[ge$x[i]]], mtcars[[ge$y[i]]], pch=19, xlab=names(mtcars)[ge$x[i]], ylab=names(mtcars)[ge$y[i]])# tesing various forms scagnostics(1:10, 1:10) scagnostics(rnorm(100), rnorm(100)) scagnostics(as.matrix(mtcars)) # more real use on a dataset s <- scagnostics(mtcars) # look at outliers o <- scagnosticsOutliers(s) o[o] # one outlier, let's plot it g <- scagnosticsGrid(s) go <- g[o,] plot(mtcars[[go$x]], mtcars[[go$y]], pch=19, xlab=names(mtcars)[go$x], ylab=names(mtcars)[go$y]) # find and plot exemplars e <- scagnosticsExemplars(s) e[e] ge <- g[e,] par(mfrow = c(2,2)) for (i in 1:dim(ge)[1]) plot(mtcars[[ge$x[i]]], mtcars[[ge$y[i]]], pch=19, xlab=names(mtcars)[ge$x[i]], ylab=names(mtcars)[ge$y[i]])