Extract the result of a single time series from an object of class beast
tsextract( x, index = 1 )
a "beast" object returned by
an integer (default to 1 ) or a vector of two integers to specify the index of the time series to extract if
A LIST object of the result for the chosen time series, which contains the same field as
Use this function only to manually and interactively examine individual times series. If the purpose is to loop through
x, the use of direct indexing is much faster. For example, if
x is a beast object for a 300x200x1000 3D array (row x col x time), use x$trend$Y[20,40,] to get the fitted trend at the pixel of row 20 and col 40.
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).
library(Rbeast) data(simdata) # handle only the 1st ts out=beast(simdata[,1]) ## Not run: # handle all the ts out=beast123(simdata, metadata=list(whichDimIsTime=1)) plot(out,1) plot(out,2) ## End(Not run)
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