pcp: Enhanced parallel coordinate plots.

In PairViz: Visualization using Graph Traversal

Enhanced parallel coordinate plots.

Description

pcp draws a parallel coordinate plot.It is a modified version of parcoord {MASS}. Variables may be reordered and panels colored in the display.

catpcp draws a parallel coordinate plot variant for categorical data.

Usage

pcp(data, order = NULL, panel.colors = NULL, col = 1, lty = 1, 
horizontal = TRUE, mar = NULL, scale = TRUE, axis.width = 0,
axis.grid.col="grey70",connect=TRUE, ...)

catpcp(data, order = NULL, pcpbars, barvars = 1:ncol(data), 
pcpbars.border = "black", pcpbars.col = NULL, pcpbars.labels = FALSE, 
pcpbars.axis.at = NULL, pcpbars.axis.labels = NULL, 
axis.width = 0.2,connect=TRUE, ...)

Arguments

data	A data frame or matrix.
order	an index vector specifying variable order. If NULL, all variables are used.
panel.colors	either a vector or a matrix of panel colors. If a vector is supplied, the ith color is used for the ith panel. If a matrix, dimensions should match those of the variables. Diagonal entries are ignored.
col	a vector of colours, recycled as necessary for each observation.
lty	a vector of line types, recycled as necessary for each observation.
horizontal	If TRUE, orientation is horizontal.
mar	margin parameters, passed to par.
scale	If TRUE, the variables are scaled to the unit interval.
axis.width	Width of each of the parallel axes.
axis.grid.col	Color of variable axes. Use NULL for no axes.
connect	If FALSE, line segments are not connected. Points are drawn if axis.width=0.
pcpbars	A list, with one component per barvar. Component i is a matrix with the bottom and top of the bars for that variable.
barvars	Categorical variables where overlayed bars show the level frequency.
pcpbars.border	Border colour of the bars.
pcpbars.col	Colour of the bars.
pcpbars.labels	Labels for the bars.
pcpbars.axis.at	Axis label positions for the bars.
pcpbars.axis.labels	Axis label text for the bars.
...	other parameters, passed to pcp by catpcp

Examples

require(PairViz) 		
y <- as.data.frame(as.table(HairEyeColor))

colvar <- 3 # any of 1:3 will do
y <- y[order(y[,colvar]),] # ensures that cases are ordered by colour within each factor level
ylong <- apply(y[,-4],2, function(x) rep(x,times=y[,4]))

cols <- desaturate_color(rainbow(4,alpha=0.3),.5)
cols <- cols[as.numeric(as.factor(ylong[,colvar]))]




ds <- factor_spreadout(ylong)




dev.new(width=5,height=2.5)
par(mar=c(2,1,2,1))
par( cex.axis=.8,cex=.8)



catpcp(ds$data,col=cols,lwd=2,pcpbars=ds$bars,pcpbars.labels=TRUE,main="Hair Eye data")

	
	
	
	
	

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