mvole {Rcapture} | R Documentation |
This data set contains robust design capture history data for adult male meadow voles.
data(mvole)
c11
, c12
, c13
, c14
, c15
c21
, c22
, c23
, c24
, c25
c31
, c32
, c33
, c34
, c35
c41
, c42
, c43
, c44
, c45
c51
, c52
, c53
, c54
, c55
c61
, c62
, c63
, c64
, c65
The data set is extracted from Table 19.1 of Williams, Nichols and Conroy (2002). The capture occasions represent five consecutive days of trapping each month from June to Decembre 1981 at Patuxent Wildlife Research Center, Laurel, Maryland.
This data set's format is the default one, i.e. each row represents the capture history of one animal.
In this data set, ten animals are in fact not released after capture. These trap deaths are not identified.
Williams, B.K., Nichols, J.D., and Conroy, M.J. (2002) Analysis and Management of Animal Populations, Academic Press: San Diego.
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/
# First, a between primary period Jolly-Seber analysis is obtained. data(mvole) mvole.pp<-periodhist(mvole,vt=rep(5,6)) op.m1<-openp(mvole.pp,dfreq=TRUE) plot(op.m1) # There is one large residual, removing the corresponding capture history # from the analysis does not change the results. The model fits well. keep2<-residuals(op.m1$glm,type="pearson")<4 op.m2<-openp(mvole.pp,dfreq=TRUE,keep=keep2) op.m2$model.fit # To find a suitable model within each primary period, the function closedp # can be used repeatedly. Heterogeneity is detected in all periods except # the second one where the data collection was perturbed (the last capture # occasion doesn't have any new capture and is taken out of the analysis). # In a robust design, we use Mh models for all primary periods bearing in # mind the questionable fit in the second one. Since there is no time effect # within primary periods, we use the function robustd.0 to fit the model. ### The following command might take a few minutes to run. rd.m1<-robustd.0(mvole[,-10], vt=c(5,4,rep(5,4)),vm="Mh",vh="Chao") rd.m1$model.fit rd.m1$emig.fit # The test for temporary immigration is not significant meaning that capture # probabilities estimated with the Jolly-Seber model are not different from # those estimated with the individual closed population models. The # differences, on the logit scale, of the Jolly-Seber minus the closed # population models capture probabilities are rd.m1$emig.param # Even in period 2, where the closed population model does not fit well, the # difference on the logit scale is non significant (estimate=.59, s.e.=1.12). # The following command allows to fit a robust design that does not specify # any model for the second period. ### The following command might take a few minutes to run. rd.m3<-robustd.0(mvole[,-10], vt=c(5,4,rep(5,4)),vm=c("Mh","none","Mh","Mh", "Mh","Mh"),vh="Chao") # With Darroch's model, the closed population estimates of the capture # probabilities are significantly smaller than those obtained from the # Jolly-Seber model. This cannot be interpreted as indicating temporary # immigration. This suggests that Darroh's model is not appropriate within # primary sessions. # The smallest AIC is obtained with the Poisson model, with parameter a=1.5 # within sessions. rd.m4<-robustd.0(mvole[,-10], vt=c(5,4,rep(5,4)),vm="Mh",vh="Poisson",va=1.5) # The estimators of the demographic parameters obtained with the robust design # are similar to those obtained with the Jolly-Seber model applied to the # between primary period data. cbind(op.m1$survivals,rd.m4$survivals) cbind(op.m1$N,rd.m4$N) cbind(op.m1$birth,rd.m4$birth) cbind(op.m1$Ntot,rd.m4$Ntot)