Rcapture-package: Loglinear Models for Capture-Recapture Experiments
Description
Estimation of abundance and other demographic parameters for closed populations, open populations and the robust design in capture-recapture experiments using loglinear models.Details
ll{
Package: Rcapture
Type: Package
Version: 1.2-1
Date: 2012-03-28
License: GPL-2
}
DESCRIPTION OF THE ACCEPTED DATA SET FORMATS
A capture-recapture data set is given to the various Rcapture functions through the X argument. X must be a numeric matrix. The arguments dfreq and dtype indicate the format of the matrix. Each have two possible values, meaning that a total of four data set formats are possible with Rcapture.
1- If dfreq=FALSE and dtype="hist", the default, X has one row per unit captured in the experiment. Each row is an observed capture history. It must contain only zeros and ones; the number one indicates a capture. In this case, the number of columns in the table represents the number of capture occasions in the experiment (noted $t$). Here is a fictive example of a data set of this type for $t=2$:
1 1
1 1
1 0
1 0
1 0
1 0
0 1
2- If dfreq=TRUE and dtype="hist", X contains one row per capture history followed by its frequency. In that case, X has $t$+1 columns. As for the format presented previously, the first $t$ columns of X, identifying the capture histories, must contain only zeros and ones. The number one indicates a capture. In this format, the example data set is represented by the following matrix:
1 1 2
1 0 4
0 1 1
3- If dfreq=FALSE and dtype="nbcap", X is simply a vector of numbers of captures. The length of the vector is $n$, the number of captured units. In this format, the example data set looks like:
2 2 1 1 1 1 1
4- If dfreq=TRUE and dtype="nbcap", X is a 2 columns matrix. The first column contains the numbers of captures, the second columns contains the observed frequencies. In this format, the example data is:
2 2
1 5
Only few functions have the dtype argument. Functions without dtype argument accept only a data matrix X of the form dtype="hist". So the first two formats listed above are the most commun.
Formats with dtype="nbcap" are useful for experiments with a large number of capture occasions $t$. Often, no units will be caugth a large number of times, and the data set will contain no observations for $t$ captures. Therefore, the number of capture occasions $t$ cannot be deduced from X as it can be when dtype="hist". So if one gives a data matrix X with dtype="nbcap", one must also provide t, the number of capture occasions, as an additional argument.
For now, the data formats with dtype="nbcap" are not generalized to the robust design. So dtype is not an argument of the robustd.0 function.References
Baillargeon, S. and Rivest, L.P. (2007) Rcapture: Loglinear models for capture-recapture in R. Journal of Statistical Software, 19(5), http://www.jstatsoft.org/v19/i05.
Chao, A. (1987) Estimating the population size for capture-recapture data with unequal catchability. Biometrics, 43(4), 783--791.
Cormack, R. M. (1985) Example of the use of glim to analyze capture-recapture studies. In Lecture Notes in Statistics 29: Statistics in Ornithology, Morgan, B. et North, P. editors, New York,: Springer-Verlag, 242--274.
Cormack, R. M. (1989) Log-linear models for capture-recapture. Biometrics, 45, 395--413.
Cormack, R. M. (1992) Interval estimation for mark-recapture studies of closed populations. Biometrics, 48, 567--576.
Cormack, R. M. (1993) Variances of mark-recapture estimates. Biometrics, 49, 1188--1193.
Cormack, R. M. and Jupp, P. E. (1991) Inference for Poisson and multinomial models for capture-recapture experiments. Biometrika, 78(4), 911--916.
Rivest, L.P. and Levesque, T. (2001) Improved log-linear model estimators of abundance in capture-recapture experiments. Canadian Journal of Statistics, 29, 555--572.
Rivest, L.P. and Daigle, G. (2004) Loglinear models for the robust design in mark-recapture experiments. Biometrics, 60, 100--107.
Rivest, L.P. and Baillargeon, S. (2007) Applications and extensions of Chao's moment estimator for the size of a closed population. Biometrics, 63(4), 999--1006.
Rivest, L.P. (2008) Why a time effect often has a limited impact on capture-recapture estimates in closed populations. Canadian Journal of Statistics, 36(1), 75--84.