secrdemo | R Documentation |
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)
data(secrdemo)
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 |
Efford, M. G. (2012) DENSITY 5.0: software for spatially explicit capture–recapture. Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand. https://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.
capthist
, read.capthist
## 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)
## 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,,,]
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