Filter and Analyze Generalised Telemetry Data from Organisms
Add Organism Data to a Detection Dataframe
Create Potential Blanking Periods for Identifying Optimal Blanking Per...
Build Continuous Residence Events
Calculate Convergence Thresholds for the rSSR curve
Compare the duration of Potential Blanking Periods
Basic Two Hit Filter for Detections
Basic Four Hit Filter for Detections
Format Detections for filteRjsats
Format Organism Data for add_org()
Format for Receiver data for filteRjsats
Idenitfy the Optimum MBP based on Convergence Threshold
Apply the "prefilter" to a Detection Dataframe
Calculate the Renormalized Sum of Squared Residuals
Residency Survival Plot
Plot the rSSR Curve and Convergence Thresholds and Optimum MBP
Add in Global Variables
Setup a Detection Dataframe for Identifying the Optimal Blanking Perio...
Analyze telemetry datasets generalized to allow any technology. The filtering steps check for false positives caused by reflected transmissions from surfaces and false pings from other noise generating equipment. The filters are based on JSATS filtering algorithms found in package 'filteRjsats' <https://CRAN.R-project.org/package=filteRjsats> but have been generalized to allow the user to define many of the filtering variables. Additionally, this package contains scripts used to help identify an optimal maximum blanking period as defined in Capello et al (2015) <doi:10.1371/journal.pone.0134002>. The functions were written according to their manuscript description, but have not been reviewed by the authors for accuracy. It is included here as is, without warranty.