openp {Rcapture} | R Documentation |
This function computes various demographic parameters using a loglinear model for open populations in capture-recapture experiments.
openp(X, dfreq=FALSE, m=c("up","ep"), neg=TRUE, keep=rep(TRUE,2^I-1)) ## S3 method for class 'openp': print(x, ...) ## S3 method for class 'openp': plot(x, main="Scatterplot of Pearson Residuals", ...)
X |
The matrix of the observed capture histories (see Rcapture-package for a description of the accepted formats). |
dfreq |
A logical. By default FALSE, which means that X has one row per unit. If TRUE, it indicates that the matrix X contains frequencies in its last column. |
m |
This argument is a character string taking the value "up" (up = unconstrained probabilities) or "ep" (ep = equal probabilities). If m is set to "up" (the default), no constraint is fixed on the loglinear parameters. Therefore some loglinear and demographic parameters are not estimable. On the other hand, when m takes the value "ep", the capture probabilities are set to a common value; this enables the estimation of all the parameters. |
keep |
This option is useful to fit the model on a subset of the possible capture histories. keep is a logical vector of length 2^I-1 taking the value TRUE for a history kept and FALSE for a history put aside. In this vector, the order of the capture histories is as defined in the histpos.t function. By default, every capture history is kept. |
neg |
If this option is set to TRUE, relevant negative gamma parameters are set to zero. This insures that the estimated survival probabilities belong to [0, 1] and that the births are positive. |
x |
An object, produced by the openp function, to print or to plot. |
main |
A main title for the plot |
... |
Further arguments to be passed to methods (see print.default and plot.default ). |
The function openp
generates statistics to test the presence of a trap effect.
The plot.openp
function produces a scatterplot of the Pearson residuals of the model versus the frequencies of capture.
If the data matrix X
was obtained through the periodhist
function, the dfreq
argument must be set to TRUE.
Standard errors are calculated by linearization.
n |
The number of captured units |
model.fit |
A table containing the deviance, degrees of freedom and AIC of the fitted model. |
trap.fit |
A table containing, for the models with an added trap effect, the deviance, degrees of freedom and AIC. |
trap.param |
The estimated trap effect parameters and their standard errors. For m="up", the I-3 first rows of trap.param are estimations of the differences logit(capture probability after a capture)-logit(capture probability after a miss) for periods 3 to I-1. The last row gives a pooled estimate of these differences calculated under the assumption that they are homogenous. |
capture.prob |
The estimated capture probabilities per period and their standard errors. |
survivals |
The estimated survival probabilities between periods and their standard errors. |
N |
The estimated population sizes per period and their standard errors. |
birth |
The estimated number of new arrivals in the population between periods and their standard errors. |
Ntot |
The estimated total number of units who ever inhabited the survey area and its standard error. |
glm |
The 'glm' object obtained from fitting the loglinear model |
loglin.param |
The loglinear model parameters estimations and their standard errors, calculated by the glm function. |
u.vector |
The Ui statistics, useful for the survival probabilities calculation, and their standard errors |
v.vector |
The Vi statistics, useful for the population sizes estimation, and their standard errors |
cov |
The covariance matrix of all the demographic parameters estimates. |
neg |
The position of the gamma parameters set to zero in the loglinear parameter vector. |
If your data contains more than one capture occasion within primary periods, use the periodhist
function to create the input data matrix X
needed by the openp
function.
This function uses the glm
function of the stats
package.
Sophie Baillargeon Sophie.Baillargeon@mat.ulaval.ca and
Louis-Paul Rivest Louis-Paul.Rivest@mat.ulaval.ca
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.
Rivest, L.P. and Daigle, G. (2004) Loglinear models for the robust design in mark-recapture experiments. Biometrics, 60, 100–107.
data(duck) op.m1 <- openp(duck, dfreq=TRUE) plot(op.m1) # To remove the capture history 111111. keep2 <- apply(histpos.t(6),1,sum)!=6 op.m2 <- openp(duck, dfreq=TRUE, keep=keep2) op.m2 # To remove the capture histories with 5 captures or more keep3 <- apply(histpos.t(6),1,sum)<5 op.m3 <- openp(duck, dfreq=TRUE, keep=keep3) op.m3 data(mvole) mvole.op<-periodhist(mvole,vt=rep(5,6)) openp(mvole.op, dfreq=TRUE)