scagnostics | R Documentation |
Scagnostics (scatterplot diagnostics) summarize potentially interesting patterns in 2d scatterplots.
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 <leland.wilkinson@gmail.com>, Anushka Anand (University of Illinois at Chicago), R package: Simon Urbanek <simon.urbanek@r-project.org>
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]])
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