knitr::opts_chunk$set(echo = TRUE, message = FALSE, fig.width = 6, fig.height = 4)

The BCPA was originally formulated to analyze irregular movement data collected on marine mammals, but in essence it simply reduced movement data (X-Y-Time) to a univariate time-series. There are - in my opinion - better (i.e. more informative and more robust) tools for dealing with movement data specifically, (e.g. at https://github.com/EliGurarie/smoove), but the BCPA might still be useful for irregular univariate time series. An example (again from marine mammals) is depth data. A recent update to BCPA makes this analysis somewhat smoother. Here is an example on simulated data.

Note - to date this is available only on the GitHub version of BCPA, i.e. the first step is:

require(devtools) install_github("EliGurarie/bcpa")

The code for this example can also be found in the help file for the `WindowSweep()`

function.

Load `bcpa`

, and a few other handy packages:

require(magrittr) require(lubridate) require(bcpa)

We simulate some data with four phases / three change points: surface to medium to deep to surface, that occur at fixed times.

set.seed(42) n.obs <- 100 time = (Sys.time() - dhours(runif(n.obs, 0, n.obs))) %>% sort d1 <- 50; d2 <- 100 t1 <- 25; t2 <- 65; t3 <- 85 sd1 <- 1; sd2 <- 5; sd3 <- 10 dtime <- difftime(time, min(time), units="hours") %>% as.numeric 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)

The structure of the data is very simple:

```
head(mydata)
```

Plot simulated depth data

with(mydata, plot(time, depth, type = "o"))

Perform the window sweep. Note that you specify the response variable (`depth`

) and the time variable (`time`

):

depth.ws <- WindowSweep(mydata, variable = "depth", time.var = "time", windowsize = 25, windowstep = 1, progress=FALSE)

Here are some plots and the summary of the change point analysis:

plot(depth.ws, ylab = "Depth (m)") plot(depth.ws, type = "flat", cluster = 8, ylab = "Depth (m)") ChangePointSummary(depth.ws, cluster = 8)

This is a pretty artificial example, but it works well.

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