bcpa-package {bcpa} | R Documentation |
A collection of functions that allows one to perform the behavioral change point analysis (BCPA) as described by Gurarie et al. (2009, Ecology Letters, 12: 395-408). The key features are estimation of discrete changes in time-series data, notable linear and turning components of gappy velocity times series extracted from movement data.
There is a fairly detailed vignette accesible by entering vignette("bcpa")
. Alternatively, the key analysis function is WindowSweep
, and reading its documentation is a good way to start using this package. This function uses a suite of functions that might also be useful for more narrow analysis, listed hierarchically (from bottom-up) below:
GetRho | maximizes the likelihood to estimate autocorrelation rho or characteristic time-scale tau. |
GetDoubleL | estimates the paramters and returns the log-likelhood at either side of a given break |
GetBestBreak | finds the single best change point according to the likelihood returned by GetDoubleL |
GetModels | uses a (modified) BIC model selection for all combinations from M0 (μ_1 = μ_2, σ_1 = σ_2, ρ_1 = ρ_2) to M7 (μ_1 \neq μ_2, σ_1 \neq σ_2, ρ_1 \neq ρ_2) to characterize the "Best Break" |
WindowSweep | sweeps a longer time series with the Best Break / Model Selection analysis, identifying most likely break points and BIC selected models across the time series. |
Summary, diagnostic, and plotting functions are:
PartitionParameters | outputs the estimated parameters of a bcpa. |
ChangePointSummary | provides a summary table of the chage points. |
plot.bcpa | a plotting method for visualizing the time series with vertical lines as change points. |
PathPlot | a method for drawing a color-coded track of the analysis. |
DiagPlot | diagnostic plots for BCPA. |
A few preprocessing functions available:
plot.track | method for plotting a generic "track" object. |
GetVT | returns step-lenghts, absolute and turning angles from track data. |
Package: | bcpa |
Type: | Package |
Version: | 1.0 |
Date: | 2013-10-23 |
License: | Unlimited |
LazyLoad: | yes |
This is a suite of functions needed to perform a complete behavioral change point analysis.
Eliezer Gurarie <eliezg@uw.edu>
Gurarie, E., R. Andrews and K. Laidre. 2009. A novel method for identifying behavioural changes in animal movement data. Ecol. Lett. 12: 395-408.
# Running through a complete analysis here: ## loading the data data(Simp) ## plotting the track (using the plot.track method) plot(Simp) ## Obtaining the movement summary table (with turning angles and step lengths) Simp.VT <- GetVT(Simp) ## Applying the analysis Simp.ws <- WindowSweep(Simp.VT, "V*cos(Theta)", windowsize = 50, windowstep = 1, progress=TRUE) ## plotting outpots plot(Simp.ws, threshold=7) plot(Simp.ws, type="flat", clusterwidth=3) PathPlot(Simp, Simp.ws) PathPlot(Simp, Simp.ws, type="flat") ## Diagnostic of assumptions DiagPlot(Simp.ws)