LPCM (version 0.41-6)

plot.lpc: Plotting local principal curves

Description

Takes an object of class lpc or lpc.spline and plots any subset of the following components of the local principal curve: Centers of mass; the curve connecting the local centers of mass; the cubic spline representation of the curve; the projections onto the curve; the starting points.

Usage

## S3 method for class 'lpc':
plot(x, type, unscale = TRUE, lwd = 1, datcol = "grey60", 
    datpch = 21, masscol = 2, masspch = 15, curvecol = 1, splinecol = 3, 
    projectcol = 4, startcol = 1:3,  startpch=NULL,...)  
## S3 method for class 'lpc.spline':
plot(x, type, unscale = TRUE, lwd = 1, datcol = "grey60", 
    datpch = 21, masscol = 2, masspch = 15, curvecol = 1, splinecol = 3, 
    projectcol = 4, startcol = 1:3,  startpch=NULL,...)

Arguments

x

an object of class lpc or lpc.spline.

type

a vector of type c("mass", "spline",...) with possible entries mass, curve, spline, project, start.

unscale

if TRUE, then data (and all fitted componens) are scaled back to their original scale; otherwise the scaled data are plotted (only relevant if scaled=TRUE in the fitted object).

lwd

width of curves.

datcol

color of data points.

datpch

plotting symbol for data points.

masscol

color of centers of mass.

masspch

plotting symbol for centers of mass.

curvecol

color of the curve interpolating the local centers of mass (this is the "local principal curve"!).

splinecol

color of the spline representation of the local principal curve.

projectcol

color of projections onto the spline representation of the local principal curve.

startcol

color of the plotted starting points.

startpch

plotting symbol for starting points; needs to be either a single symbol, or a vector of symbols of the same length as the number of starting points.

...

further arguments passed to plot or scatterplot3d.

Value

• A 2D plot, 3D plot, or a pairs plot (depending on the data dimension $d$.).

  With increasing dimension $d$, less plotting options tend to be supported. The nicest plots are obtained for $d=2$ and $d=3$.

Warning

This function computes all missing information (if posssible), so computation will take the longer the less informative the given object is, and the more advanced aspects are asked to plot!

References

Einbeck, J., Tutz, G., and Evers, L. (2005). Local principal curves. Statistics and Computing 15, 301-313.

Einbeck, J., Evers, L. & Hinchliff, K. (2010): Data compression and regression based on local principal curves. In A. Fink, B. Lausen, W. Seidel, and A. Ultsch (Eds), Advances in Data Analysis, Data Handling, and Business Intelligence, Heidelberg, pp. 701--712, Springer.

See Also

lpc, lpc.spline

Examples

data(calspeedflow)
lpc1 <- lpc(calspeedflow[,3:4])
plot(lpc1, type=c("spline","project"), lwd=2)