secr (version 3.0.1)

secrdemo: SECR Models Fitted to Demonstration Data

Description

Demonstration data from program Density are provided as text files in the `extdata' folder, as raw dataframes (trapXY, captXY), and as a combined capthist object (captdata) ready for input to secr.fit.

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)

Usage

data(secrdemo)

Details

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).

Dataframe trapXY contains the data from the Density input file `trap.txt', and captXY contains the data from `capt.txt' (Efford 2012).

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.

Object	Description
captXY	data.frame of capture data
trapXY	data.frame of trap locations
captdata	capthist object
secrdemo.0	fitted secr model -- null
secrdemo.b	fitted secr model -- g0 trap response
secrdemo.CL	fitted secr model -- null, conditional likelihood

References

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.

See Also

capthist, read.capthist

Examples

## 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", detector = "single")
# 
# ## 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)
## ---------------------------------------------

## 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,,,]