greenplot: Plot Statistics for a Greenclust Object In JeffJetton/greenclust: Combine Categories Using Greenacre's Method

Description

Displays a connected scatterplot showing the r-squared values (x-axis) and p-values (y-axis) at each clustering step of a `greenclust` object. Points are labeled with their cutpoints, i.e., the number of groups/clusters found at each step. The point with the lowest p-value (typically the optimal cutpoint) is highlighted.

Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```greenplot( g, type = "b", bg = "gray75", pch = 21, cex = 1, optim.col = "red", pos = 2, main = "P-Value vs. R-Squared for Num. Clusters", xlab = "r-squared", ylab = NULL, ... ) ```

Arguments

 `g` an object of the type produced by `greenclust` `type` 1-character string giving the type of plot desired: "p" for points, "l" for lines, and "b" (the default) for both points and lines. `bg` a vector of background colors for open plot symbols. Also used for the line color if type is "b". `pch` a vector of plotting characters or symbols: see `points` `cex` a numerical vector giving the amount by which plotting characters and symbols should be scaled relative to the default. For this plot, the numeric labels on each point are always scaled to 0.80 of this value. `optim.col` color to use for highlighting the "optimal" cutpoint. `pos` specifies the position of labels relative to their points: 1 = below, 2 = left, 3 = above, and 4 = right. `main` an overall title for the plot. `xlab` a title for the x axis. `ylab` a title for the y axis. `...` additional arguments to be passed to the plotting methods.

References

Greenacre, M.J. (1988) "Clustering the Rows and Columns of a Contingency Table," Journal of Classification 5, 39-51. https://doi.org/10.1007/BF01901670

`greenclust`, `greencut`, `assign.cluster`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# Combine Titanic passenger attributes into a single category # and create a contingency table for the non-zero levels tab <- t(as.data.frame(apply(Titanic, 4:1, FUN=sum))) tab <- tab[apply(tab, 1, sum) > 0, ] grc <- greenclust(tab) greenplot(grc) # Plot using custom graphical parameters greenplot(grc, type="p", bg="lightblue", optim.col="darkorange", pos=3, bty="n", cex.main=2, col.main="blue") ```