window

This project introduces a sliding window analysis function for the ctmm R package.

Features

• 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 ctmm package's existing framework
• Plot point estimats, confidence intervals and (optional) covariates to visualize correlation

Getting Started

Prerequisites

• R
• The ctmm package installed.

Installation

Clone the repository and set up the environment: bash git clone https://github.com/ctmm-initiative/window.git cd window

Arguments

• 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)

Usage

Create an object of class TS using the animal tracking data, and then use plot() to visualize the TS object ```r library(ctmm)

# Download example data data(buffalo)

# Individual example dataset individualdata <- buffalo$Cilla individualGUESS <- ctmm.guess(individual_data, interactive = FALSE)

# Population example dataset populationdata <- buffalo[c(1, 3, 6)] populationGUESS <- lapply(populationdata, function(populationdata) ctmm.guess(population_data, interactive = FALSE))

# Arguments mintimestep <- as.difftime(10, units = "days") window <- as.difftime(30, units = "days")

# Create TS of individual window estimates individualTS <- slide(data = individualdata, CTMM = individualGUESS, window = window, dt.min = mintime_step, recycle = TRUE)

# Create TS of population window estimates populationTS <- slide(data = populationdata, CTMM = populationGUESS, window = window, dt.min = mintime_step, recycle = TRUE)

# plot results plot(populationTS) plot(individualTS)

```

Acknowledgments

• This project was developed by Michael Garan under the guidance of Dr. Christen Fleming.

Contact

For questions or feedback, contact: Michael Garan
Email: michael.d.garan@gmail.com