closedp | R Documentation |
The functions closedp.t
and closedp.0
fit various loglinear models for closed populations in
capture-recapture experiments. For back compatibility, closedp.t
is also named closedp
.
closedp.t
fits more models than closedp.0
but for data set with more than 20 capture occasions, the function might fail. However, closedp.0
works with fairly large data sets (see Details).
closedp(X, dfreq=FALSE, neg=TRUE, ...) closedp.t(X, dfreq=FALSE, neg=TRUE, ...) closedp.0(X, dfreq=FALSE, dtype=c("hist","nbcap"), t=NULL, t0=NULL, neg=TRUE, ...) ## S3 method for class 'closedp' print(x, ...) ## S3 method for class 'closedp' boxplot(x, main="Boxplots of Pearson Residuals", ...) ## S3 method for class 'closedp' plot(x, main="Residual plots for some heterogeneity models", ...)
X |
The matrix of the observed capture histories (see |
dfreq |
A logical. By default |
dtype |
A characters string, either |
t |
Requested only if |
t0 |
A numeric. Models are fitted considering only the frequencies of units captured 1 to |
neg |
If this option is set to TRUE, negative eta parameters in Chao's lower bound models are set to zero (see Details). |
... |
Further arguments to be passed to |
x |
An object, produced by a |
main |
A main title for the plot. |
closedp.t
fits models M0, Mt, Mh Chao (LB), Mh Poisson2, Mh Darroch, Mh Gamma3.5,
Mth Chao (LB), Mth Poisson2, Mth Darroch, Mth Gamma3.5, Mb and Mbh. closedp.0
fits
only models M0, Mh Chao (LB), Mh Poisson2, Mh Darroch and Mh Gamma3.5. However,
closedp.0
can be used with larger data sets than closedp.t
.
This is explained by the fact that closedp.t
fits models using the frequencies of
the observable capture histories (vector of size 2^t-1), whereas closedp.0
uses the numbers of units captured i times, for i=1,…,t (vector of size t).
See Rcapture-package
for more details about the distinction between .t
and .0
functions.
Multinomial profile confidence intervals for the abundance are constructed by closedpCI.t
and closedpCI.0
.
To calculate bias corrected abundance estimates, use the closedp.bc
function.
CHAO'S LOWER BOUND MODEL
Chao's (or LB) models estimate a lower bound for the abundance, both with a time effect (Mth Chao) and without one (Mh Chao). The estimate obtained under Mh Chao is Chao's (1987) moment estimator. Rivest and Baillargeon (2007) exhibit a loglinear model underlying this estimator and provide a generalization to Mth. For these two models, a small deviance means that there is an heterogeneity in capture probabilities; it does not mean that the lower bound estimate is unbiased. To test whether a certain model for heterogeneity is adequate, one can conduct a likelihood ratio test by subtracting the deviance of Chao's model to the deviance of the heterogeneous model under study. If this heterogeneous model includes a time effect, it must be compared to model Mth Chao. If it does not include a time effect, it must be compared to model Mh Chao. Under the null hypothesis of equivalence between the two models, the difference of deviances follows a chi-square distribution with degrees of freedom equal to the difference between the models' degrees of freedom.
Chao's lower bound models contain t-2 parameters, called
eta parameters, for the heterogeneity. These parameters should theoretically be greater
or equal to zero (see Rivest and Baillargeon (2007)). When the argument neg
is set
to TRUE
(the default), negative eta parameters are set to zero (to do so, columns are
removed from the design matrix of the model). Degrees of freedom of Chao's model increase
when eta parameters are set to zero.
OTHER MODELS FOR HETEROGENEITY
Other models for heterogeneity are defined as follows :
Model | Column for heterogeneity in the design matrix |
Poisson2 | 2^k-1 |
Darroch | k^2/2 |
Gamma3.5 | -log(3.5 + k) + log(3.5) |
where k is the number of captures. Poisson and Gamma models with alternative to the
parameter defaults values 2 and 3.5 can be fitted with the closedpCI.t
and
closedpCI.0
functions.
Darroch's models for Mh and Mth are considered by Darroch et al. (1993) and Agresti (1994).
Poisson and Gamma models are discussed in Rivest and Baillargeon (2007). Poisson models
typically yield smaller corrections for heterogeneity than Darroch's model since the capture
probabilities are bounded from below under these models. On the other hand, Gamma models
can lead to very large estimators of abundance. We suggest considering this estimator only in
experiments where very small capture probabilities are likely.
PLOT METHODS AND FUNCTIONS
The boxplot.closedp
function produces boxplots of the Pearson residuals of the fitted loglinear models that converged.
The plot.closedp
function produces scatterplots of the Pearson residuals in terms of fi
(number of units captured i times) for the heterogeneous models Mh Poisson2, Mh Darroch and Mh Gamma3.5 if they converged.
n |
The number of captured units. |
t |
The total number of capture occasions in the data matrix |
t0 |
For |
results |
A table containing, for every fitted model:
|
bias |
A vector, the asymptotic bias of the estimated population size for every fitted model. |
glm |
A list of the 'glm' objects obtained from fitting models. |
glm.err |
A list of character string vectors. If the |
glm.warn |
A list of character string vectors. If the |
parameters |
Capture-recapture parameters estimates. It contains N, the estimated population size, and p or p1 to pt defined as follows for the different models :
For models Mb and Mbh, it also contains c, the recapture probability at any capture occasion. |
neg.eta |
The position of the eta parameters set to zero in the loglinear parameter
vector of models MhC and MthC. |
X |
A copy of the data given as input in the function call. |
dfreq |
A copy of the |
This function uses the glm
function of the stats package.
Louis-Paul Rivest Louis-Paul.Rivest@mat.ulaval.ca and Sophie Baillargeon
Agresti, A. (1994) Simple capture-recapture models permitting unequal catchability and variable sampling effort. Biometrics, 50, 494–500.
Baillargeon, S. and Rivest, L.P. (2007) Rcapture: Loglinear models for capture-recapture in R. Journal of Statistical Software, 19(5), doi: 10.18637/jss.v019.i05.
Chao, A. (1987) Estimating the population size for capture-recapture data with unequal catchabililty. Biometrics, 45, 427–438.
Darroch, S.E., Fienberg, G., Glonek, B. and Junker, B. (1993) A three sample multiple capture-recapture approach to the census population estimation with heterogeneous catchability. Journal of the American Statistical Association, 88, 1137–1148.
Rivest, L.P. and Levesque, T. (2001) Improved loglinear model estimators of abundance in capture-recapture experiments. Canadian Journal of Statistics, 29, 555–572.
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.
Seber, G.A.F. (1982) The Estimation of Animal Abundance and Related Parameters, 2nd edition, New York: Macmillan.
closedpCI.t
, closedpCI.0
, closedp.bc
, closedp.Mtb
, closedpMS.t
, uifit
.
# hare data set hare.closedp <- closedp.t(hare) hare.closedp boxplot(hare.closedp) # Third primary period of mvole data set period3 <- mvole[, 11:15] closedp.t(period3) # BBS2001 data set BBS.closedp <- closedp.0(BBS2001, dfreq = TRUE, dtype = "nbcap", t = 50, t0 = 20) BBS.closedp plot(BBS.closedp) ### Seber (1982) p.107 # When there is 2 capture occasions, the heterogeneity models cannot be fitted X <- matrix(c(1,1,167,1,0,781,0,1,254), byrow = TRUE, ncol = 3) closedp.t(X, dfreq = TRUE) ### Example of captures in continuous time # Illegal immigrants data set closedp.0(ill, dtype = "nbcap", dfreq = TRUE, t = Inf)
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