# Perform window sweep for BCPA This is the workhorse function of the BCPA. It performs a sweep of the time series, searching for most significant change points and identifying the parsimonious model according to an adjusted BIC. ```r WindowSweep( data, variable, time.var = "T.mid", windowsize = 50, windowstep = 1, units = "hours", K = 2, tau = TRUE, range = 0.6, progress = TRUE, plotme = FALSE, ... ) ``` ## Arguments - `data`: the data to be analyzed. Generically, the output of the `GetVT` function containing step lengths, absolute and turning angles, etc, but can be any data frame with a column representing times and any column (or set of columns) that can be converted to a time series for change point analysis. - `variable`: a character string representing the response variable to apply the BCPA to. For example `"V*cos(theta)"` for persistence velocity, `"log(V)"` for log of velocity if the data are outputs of `codeGetVT`. Otherwise, any column (e.g. `"Depth"`) in the data. - `time.var`: character string for the time variable. The default is `T.mid` from the output of the `codeGetVT`. - `windowsize`: integer size of the analysis window as a number of data points (not time units). Should probably be no smaller than 20. - `windowstep`: integer step size of analysis. Values greater than 1 speed the analysis up. - `units`: if the times are POSIX, `units` (one of "secs", "mins", "hours", "days", "weeks") determine the unit of the `windowstep`. - `K`: sensitivity parameter for the adjusted BIC. Smaller values make for a less sensitive model selection, i.e. more likely that the null model of no significant changes will be selected. - `tau`: a logical indicating whether the autocorrelation "rho" or the characteristic time "tau" should be estimated. - `range`: a number between 0 and 1 that determines the extent of each window that is scanned for changepoints. I.e., if the window is 100 datapoints long, at the default `range=0.6`, changepoints will be scanned between 20 and 80. - `progress`: logical - whether or not to output a progress bar as the analysis is being performed. - `plotme`: logical - whether or not to plot the analysis as it is happening. This slows the analysis considerably, especially in non-dynamic graphic environments like RStudio. - `...`: additional parameters to be passed to the `PartitionParameters` function. ## Returns an object of class `windowsweep`, which is a list containing: - **ws**: a data frame containing the change point, selected model, and parameter estimates - **x**: the response variable - **t**: the time variable - in the units specified in the data object - **t.POSIX**: the time variable as a POSIX object (containing Y-M-D H:S) - **windowsize**: the window size - **windowstep**: the window step size ## Examples ```r # Using the included simulated movement data require(bcpa) data(Simp) plot(Simp) Simp.VT <- GetVT(Simp) ## note: column names of Simp.VT include: ### "S" - step length ### "Theta" - turning angle ### T.start" "T.end" "T.mid" "T.POSIX" - time variables if(interactive()) Simp.ws <- WindowSweep(Simp.VT, "V*cos(Theta)", windowsize = 50, windowstep = 1, progress=TRUE) else Simp.ws <- WindowSweep(Simp.VT, "V*cos(Theta)", windowsize = 50, windowstep = 1, progress=FALSE) plot(Simp.ws, threshold = 7) plot(Simp.ws, type = "flat", clusterwidth = 3) PathPlot(Simp, Simp.ws) PathPlot(Simp, Simp.ws, type="flat") DiagPlot(Simp.ws) # Example of application on one dimensional data (e.g. depth) # simulate some data with three change points: surface, medium, deep, surface ## random times within 100 hours of now n.obs <- 100 time = sort(Sys.time() - runif(n.obs, 0, n.obs) * 60 * 60) d1 <- 50; d2 <- 100 t1 <- 25; t2 <- 65; t3 <- 85 sd1 <- 1; sd2 <- 5; sd3 <- 10 dtime <- as.numeric(difftime(time, min(time), units="hours")) phases <- cut(dtime, c(-1, t1, t2, t3, 200), labels = c("P1","P2","P3","P4")) means <- c(0,d1,d2,0)[phases] sds <- c(sd1,sd2,sd3,sd1)[phases] depth <- rnorm(n.obs,means, sds) # make all depths positive! depth <- abs(depth) mydata <- data.frame(time, depth) # perform windowsweep ws <- WindowSweep(mydata, "depth", time.var = "time", windowsize = 30, windowstep = 1, progress=interactive()) plot(ws) plot(ws, type = "flat", cluster = 6) ChangePointSummary(ws, cluster = 6) ``` ## See Also for internal functions: `GetModels`, `GetBestBreak`, `GetDoubleL`; for summarizing output: `ChangePointSummary`; for plotting output: `plot.bcpa` ## Author(s) Eliezer Gurarie

WindowSweep function