Supplement 1. Example data and R code.

<h2>File List</h2><blockquote> <p><a href="available_habitat.txt">available_habitat.txt</a> - data file representing the available habitat</p> <p><a href="gps_locations.txt">gps_locations.txt</a> - data file representing the GPS location data</p> <p><a href="Rcode.R">Rcode.R </a>- R code to analyze the example data using the RSF likelihood for GPS fix success</p> <p><a href="all_files.zip">all_files.zip</a> - all files at once</p> </blockquote><h2>Description</h2><blockquote> <p>Rcode.R analyzes the example data, which is one of the simulated data sets with 90% GPS fix success contained in the Nielson et al. paper. There are two data files (available_habitat.txt and gps_locations.txt) representing the available habitat and the GPS location data, respectively. The example data can be analyzed by saving both data files to a working directory, opening an R session and copying and pasting all text below at the R command prompt. Select quantities are output to the terminal. The description of columns in the data files is provided below.</p><p><b>In available_habitat.txt:</b></p> <p>(1) utmX = utm easting coordinate of habitat unit</p> <p>(2) utmY = utm northing coordinate of habitat unit</p> <p>(3) unit.id = habitat unit ID</p> <p>(4) prcnt.sage = % Wyoming Big Sage</p> <p>(5) elevation = elevation (km)</p> <p><b>In gps_locations.txt:</b></p> <p>(1) unit.id = habitat unit of GPS location (missing = NA)</p> <p>(2) fix.attempt = sequential fix attempt number</p> <p><b>Column sums for 'available_habitat.txt" (in order):</b></p> <p> utmX = 4.985109e+7 = 0.0000004985109<br> utmY = 8.483487e+8 = 0.00000008483487<br> unit.id = 1.7205e+4 = 0.00017205<br> prcnt.sage = 1.06875e+4 = 0.000106875<br> elevation = 3.795941e+2 = 0.03795941</p> <p><b>Column sums for "gps_locations.txt" (in order):</b></p> <p> unit.id = 42761 (note some "NA" values, which indicate missing)<br> fix.attempt = 98790</p> </blockquote