RMark (version 3.0.0)

Blackduck: Black duck known fate data

Description

A known fate data set on Black ducks that accompanies MARK as an example analysis using the Known model.

Format

A data frame with 48 observations on the following 5 variables.

ch

a character vector containing the encounter history of each bird

BirdAge

the age of the bird: a factor with levels 0 1 for young and adult

Weight

the weight of the bird at time of marking

Wing_Len

the wing-length of the bird at time of marking

condix

the condition index of the bird at time of marking

Details

This is a data set that accompanies program MARK as an example for Known fate. The data can be stratified using BirdAge as a grouping variable. The function run.Blackduck defined below in the examples creates some of the models used in the dbf file that accompanies MARK.

Note that in the MARK example the variable is named Age. In the R code, the fields "age" and "Age" have specific meanings in the design data related to time since release. These will override the use of a field with the same name in the individual covariate data, so the names "time", "Time", "cohort", "Cohort", "age", and "Age" should not be used in the individual covariate data with possibly the exception of "cohort" which is not defined for models with "Square" PIMS such as POPAN and other Jolly-Seber type models.

Examples

data(Blackduck)
# Change BirdAge to numeric; starting with version 1.6.3 factor variables are
# no longer allowed.  They can work as in this example but they can be misleading
# and fail if the levels are non-numeric.  The real parameters will remain 
# unchanged but the betas will be different.
Blackduck$BirdAge=as.numeric(Blackduck$BirdAge)-1
run.Blackduck=function()
{
#
# Process data
#
bduck.processed=process.data(Blackduck,model="Known")
#
# Create default design data
#
bduck.ddl=make.design.data(bduck.processed)
#
#  Add occasion specific data min < 0; I have no idea what it is
#
bduck.ddl$S$min=c(4,6,7,7,7,6,5,5)
#
#  Define range of models for S
#
S.dot=list(formula=~1)
S.time=list(formula=~time)
S.min=list(formula=~min)
S.BirdAge=list(formula=~BirdAge)
#
# Note that in the following model in the MARK example, the covariates
# have been standardized.  That means that the beta parameters will be different
# for BirdAge, Weight and their interaction but the likelihood and real parameter
# estimates are the same.
#
S.BirdAgexWeight.min=list(formula=~min+BirdAge*Weight)
S.BirdAge.Weight=list(formula=~BirdAge+Weight)
#
# Create model list and run assortment of models
#
model.list=create.model.list("Known")
bduck.results=mark.wrapper(model.list,data=bduck.processed,ddl=bduck.ddl,
               invisible=FALSE,threads=1,delete=TRUE)

#
# Return model table and list of models
#
return(bduck.results)
}
bduck.results=run.Blackduck()
bduck.results