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Guided parallel coordinate plot.

guided_pcp.Rd

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

References

see overview

Author

C.B. Hurley and R.W. Oldford

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...

if (FALSE) {
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)



}

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