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.

1 2 3 4 5 6 7 8 | ```
## S3 method for class 'lpc'
plot(x, type, unscale = TRUE, lwd = 1, datcol = "grey60",
datpch = 21, masscol = NULL, masspch = 15, curvecol = 1, splinecol = 3,
projectcol = 4, startcol = NULL, startpch=NULL,...)
## S3 method for class 'lpc.spline'
plot(x, type, unscale = TRUE, lwd = 1, datcol = "grey60",
datpch = 21, masscol = NULL, masspch = 15, curvecol = 1, splinecol = 3,
projectcol = 4, startcol = NULL, startpch=NULL,...)
``` |

`x` |
an object of class |

`type` |
a vector of type |

`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 |

`lwd` |
width of curves. |

`datcol` |
color of data points. |

`datpch` |
plotting symbol for data points. |

`masscol` |
color of centers of mass (see below). |

`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 |

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

The most flexible plotting option is `masscol`

. Depending on the
length of the specified vector, this will be interpreted differently. If
a scalar is provided, the corresponding color will be given to all centers of
mass. If the length of the vector is larger than 1, then this option
will assign different colours to different depths, or different branch
numbers, or to individual data points, depending on the length. The
default setting is assigning colours according to depth, in the order
red, blue, black.

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

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!

JE

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.

1 2 3 | ```
data(calspeedflow)
lpc1 <- lpc(calspeedflow[,3:4])
plot(lpc1, type=c("spline","project"), lwd=2)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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