guided_pcp: Guided parallel coordinate plot.

In PairViz: Visualization using Graph Traversal

Guided parallel coordinate plot.

Description

Draws a parallel coordinate plot, with an accompanying barchart showing an index (eg correlation, scagnostics) levels for each panel. An index legend is optional.

Usage

guided_pcp(data, edgew=NULL, path = NULL, pathw=NULL,zoom=NULL,pcpfn=pcp,
     pcp.col = 1,lwd=0.5, panel.colors=NULL, pcp.mar=c(1.5,2,2,2), pcp.scale=TRUE,
     bar.col=1:9,bar.axes=FALSE, bar.mar=NULL,bar.ylim=NULL, reorder.weights=TRUE,
    layout.heights=NULL, layout.widths=c(10,1),
     main=NULL,legend=FALSE,cex.legend = 1,legend.mar=c(1,4,1,1),...)
     

Arguments

data	A data frame or matrix.
edgew	Matrix (or vector) whose rows give index values for each pair of variables.
path	an index vector specifying variable order, or a function. If a function, find_path(edgew,path,...) constructs the index vector.
pathw	Matrix (or vector) whose rows give index values for each adjacent pair of variables in path. Usually this argument is NULL and pathw is computed from the path and edgew.
zoom	If provided, a numeric vector specifying a subsequence of path to display.
pcpfn	Function to draw the parallel coordinates.
pcp.col	Line colors.
lwd	Line widths.
panel.colors	Background panel colors, passed to thepcpfn
pcp.mar	Controls PCP margin size.
pcp.scale	If TRUE, the variables will be scaled to 0-1 range, otherwise the data is not scaled.
bar.col	Bar colors.
bar.axes	Draw barplot axes, if TRUE.
bar.mar	Controls barplot margin size.
bar.ylim	Vertical limits of bar plot.
reorder.weights	If TRUE, reorder barplot indices so large values are drawn at the bottom.
layout.heights	Controls the layout.
layout.widths	Controls the layout.
main	Main title for PCP.
legend	If TRUE, draws the barplot index legend.
cex.legend	Controls legend text size.
legend.mar	Legend margin size.
...	Optional arguments

Author(s)

C.B. Hurley and R.W. Oldford

References

see overview

See Also

pcp,catpcp

Examples

	 			
require(PairViz) 			


data <- mtcars[,c(1,3:6)]
cols <- c("red","green")[mtcars[,9]+1 ]    # transmission type, red=automatic

# add a correlation guide and find "better" hamiltonians...

# add a correlation guide...


corw <- dist2edge(as.dist(cor(data)))
edgew <- cbind(corw*(corw>0), corw*(corw<0))
         
# add a correlation guide to a PCP, positive cors shown in blue, negative in purple...

## Not run: 
dev.new(width=3,height=3)

par(cex.axis=.65)

guided_pcp(data,edgew, pcp.col=cols,
         main="Correlation guided PCP",bar.col = c("blue","purple"))

dev.new(width=7,height=3)
par(cex.axis=.65)

guided_pcp(data,edgew, path=eulerian, pcp.col=cols,lwd=2,
         main="Correlation guided Eulerian PCP",bar.col = c("blue","purple"),bar.axes=TRUE)




## End(Not run)

# Scagnostic guides are useful here- see the demos for more examples.

PairViz documentation built on Aug. 12, 2022, 5:06 p.m.