This project introduces a sliding window analysis function for the ctmm R package.
- Custom time-series object class for sliding window analysis of animal tracking data
- Extracts point estimates and confidence intervals for selected variable estimates
- Customizable window size and time step options
- Flexible functionality for individual and population level estimates
- Compatible with the
ctmmpackage's existing framework - Plot point estimats, confidence intervals and (optional) covariates to visualize correlation
- R
- The
ctmmpackage installed.
Clone the repository and set up the environment:
git clone https://github.com/ctmm-initiative/window.git
cd window- data: tracking data of ctmm class
- CTMM: Guess model used as a starting point for the model fitting process. Use ctmm.guess(data)
- variable: calculates parameter estimate : "area", "diffusion", "speed, "velocity", "position"
- dt.min: minimum time step between Time series windows as difftime object
- window: window size as difftime object
- max_windows: Option for setting a limit to window estimates calculated for the time series (TS)
- select: More rigorous method for selecting movement model by default. For faster modeling fitting make select = FALSE
- recycle: Option to use the previous model fit as a starting point for the next model fitting process in the timeseries
- Guassian: Option when estimating speed parameter (Default set to FALSE)
- covariate: Option for including covariate in the TS object class and plotting along parameter estimate (name of column)
Create an object of class TS using the animal tracking data, and then use plot() to visualize the TS object
library(ctmm)
# Download example data
data(buffalo)
# Individual example dataset
individual_data <- buffalo$Cilla
individual_GUESS <- ctmm.guess(individual_data, interactive = FALSE)
# Population example dataset
population_data <- buffalo[c(1, 3, 6)]
population_GUESS <- lapply(population_data, function(population_data) ctmm.guess(population_data, interactive = FALSE))
# Arguments
min_time_step <- as.difftime(10, units = "days")
window <- as.difftime(30, units = "days")
# Create TS of individual window estimates
individual_TS <- slide(data = individual_data, CTMM = individual_GUESS, window = window, dt.min = min_time_step, recycle = TRUE)
# Create TS of population window estimates
population_TS <- slide(data = population_data, CTMM = population_GUESS, window = window, dt.min = min_time_step, recycle = TRUE)
# plot results
plot(population_TS)
plot(individual_TS)
- This project was developed by Michael Garan under the guidance of Dr. Christen Fleming.
For questions or feedback, contact:
Michael Garan
Email: michael.d.garan@gmail.com