plot.bcpa | R Documentation |

Plotting method for the output of a BCPA analysis with vertical break points, superimposed estimates of the partitioned mean and variance estimates and color-coded autocorrelation estimates.

## S3 method for class 'bcpa' plot( x, type = c("smooth", "flat")[1], threshold = 3, clusterwidth = 1, col.cp = rgb(0.5, 0, 0.5, 0.5), pt.cex = 0.5, legend = TRUE, rho.where = "topleft", mu.where = "nowhere", col.sd = "red", col.mean = "black", ... )

`x` |
a |

`type` |
whether to plot smooth or flat bcpa output |

`threshold` |
for smooth BCPA, this is the minimum number of windows that must have identified a given changepoint to be illustrated. |

`clusterwidth` |
for flat BCPA, this is the temporal range within which change points are considered to be within the same cluster. |

`col.cp, col.mean, col.sd` |
color of the vertical change points, mean estimate, and prediction interval (mu +- sigma), respectively. |

`pt.cex` |
expansion coefficient for point sizes. |

`legend` |
logical - whether to draw a legend or not. |

`rho.where` |
where to place the legend for the time-scale / auto-correlation. Can be one of "nowhere", "top", "bottom", "left", "right", "topleft", "topright", "bottomright", "bottomleft" |

`mu.where` |
where (and whether) to place the legend box for the mean -
same options as for |

`...` |
other arguments to pass to the |

Eliezer Gurarie

Plots output of the `WindowSweep`

function.

if(!exists("Simp.ws")) { data(Simp) Simp.ws <- WindowSweep(GetVT(Simp), "V*cos(Theta)", windowsize = 50, windowstep = 1, progress=TRUE) } plot(Simp.ws) # this actually provides basically the exact original changepoints plot(Simp.ws, threshold=7) # here's a flat analysis plot(Simp.ws, type="flat", clusterwidth=3, legend=FALSE)

bcpa documentation built on May 30, 2022, 5:07 p.m.

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