plot.beast | R Documentation |

Plot the result obtained from the beast function.

```
## S3 method for class 'beast'
plot(
x,
index = 1,
vars = c('y','s','scp','sorder','t','tcp','torder','slpsgn','o','ocp','error'),
col = NULL,
main = "BEAST decomposition and changepoint detection",
xlab = 'Time',
ylab = NULL,
cex.main = 1,
cex.lab = 1,
relative.heights = NULL,
interactive = FALSE,
ncpStat = c('median','mode','mean','pct90','max'),
...
)
```

`x` |
a "beast" object returned by |

`index` |
an integer (default to 1 ) or a vector of two integers to specify the index of the time series to plot if |

`vars` |
a vector of strings indicating the elements or variables of |

`relative.heights` |
a numeric vector of the same length as that of |

`col` |
a string vector of the same length as that of |

`main` |
a string; the main title. |

`xlab` |
a string: the x axis title. |

`ylab` |
a string vector of the same length as that of |

`cex.main` |
cex for the main title |

`cex.lab` |
cex for the axis title |

`interactive` |
a bool scalar. If TRUE, an interactive GUI is used for examining individual elements of |

`ncpStat` |
character. A string to specify which statistic is used for the Number of ChangePoint (ncp). Five values are possible: 'mean', 'mode', 'median','pct90', and 'max'; the default is 'median'. Individual models sampled by BEAST has a varying dimension (e.g., number of changepoints or knots). For example, if mcmc$samples=10, the numbers of changepoints for the 10 sampled models are assumed to be c(0, 2, 4, 1, 1, 2, 7, 6, 6, 1). The mean ncp will be 3.1 (rounded to 3), the median is 2.5 (2), the mode is 1, and the maximum is 7. The 'max' option plots all the changepoints recorded in |

`...` |
additional parameters to be implemented. |

This function creates various plots to demonstrate the results of a beast decomposition. .

Zhao, K., Wulder, M.A., Hu, T., Bright, R., Wu, Q., Qin, H., Li, Y., Toman, E., Mallick, B., Zhang, X. and Brown, M., 2019. Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm. Remote Sensing of Environment, 232, p.111181 (the beast algorithm paper).

Zhao, K., Valle, D., Popescu, S., Zhang, X. and Mallick, B., 2013. Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection. Remote Sensing of Environment, 132, pp.102-119 (the Bayesian MCMC scheme used in beast).

Hu, T., Toman, E.M., Chen, G., Shao, G., Zhou, Y., Li, Y., Zhao, K. and Feng, Y., 2021. Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 176, pp.250-261(a beast application paper).

`beast`

, `beast.irreg`

, `beast123`

, `plot.beast`

,`minesweeper`

, `tetris`

, `geeLandsat`

```
library(Rbeast)
data(simdata)
## Not run:
result=beast123(simdata, metadata=list(whichDimIsTime=1))
plot(result,1)
plot(result,2)
## End(Not run)
```

Rbeast documentation built on May 31, 2023, 9:23 p.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.