Demonstration data from program Density are provided as text
files in the ‘extdata’ folder, as raw dataframes (
captXY), and as a combined
captdata) ready for input to
The fitted models are objects of class
secr formed by
secrdemo.0 <- secr.fit (captdata)
secrdemo.b <- secr.fit (captdata, model = list(g0 = ~b))
secrdemo.CL <- secr.fit (captdata, CL = TRUE)
The raw data are 235 fictional captures of 76 animals over 5 occasions in 100 single-catch traps 30 metres apart on a square grid with origin at (365,365).
trapXY contains the data from the Density input file
captXY contains the data from ‘capt.txt’ (Efford
The fitted models use a halfnormal detection function and the likelihood for multi-catch traps (expect estimates of g0 to be biased because of trap saturation Efford et al. 2009). The first is a null model (i.e. parameters constant) and the second fits a learned trap response.
|captXY||data.frame of capture data|
|trapXY||data.frame of trap locations|
|secrdemo.0||fitted secr model -- null|
|secrdemo.b||fitted secr model -- g0 trap response|
|secrdemo.CL||fitted secr model -- null, conditional likelihood|
Efford, M. G. (2012) DENSITY 5.0: software for spatially explicit capture–recapture. Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand. http://www.otago.ac.nz/density.
Efford, M. G., Borchers D. L. and Byrom, A. E. (2009) Density estimation by spatially explicit capture-recapture: likelihood-based methods. In: D. L. Thomson, E. G. Cooch and M. J. Conroy (eds) Modeling Demographic Processes in Marked Populations. Springer, New York. Pp. 255–269.
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## Not run: ## navigate to folder with raw data files olddir <- setwd (system.file("extdata", package="secr")) ## construct capthist object from raw data captdata <- read.capthist ("capt.txt", "trap.txt", fmt = "XY") ## generate demonstration fits secrdemo.0 <- secr.fit (captdata) secrdemo.CL <- secr.fit (captdata, CL = TRUE) secrdemo.b <- secr.fit (captdata, model = list(g0 ~ b)) ## restore previous setting setwd(olddir) ## End(Not run) ## display the null model fit, using the print method for secr secrdemo.0 ## compare fit of models AIC(secrdemo.0, secrdemo.b) ## display estimates for the two models (single session) collate(secrdemo.0, secrdemo.b)[1,,,]