process.data(data, begin.time = 1, model = "CJS",
mixtures = 1, groups = NULL, allgroups = FALSE,
age.var = NULL, initial.ages = c(0), age.unit = 1,
time.intervals = NULL, nocc = NULL,
strata.labels = NULL, counts = NULL, reverse = FALSE)ch which is the capture (encounter) history stored
as a character string. data can also have a field
freq which is the number of animals with that
capture histormark for a list of possible values for
modeldata that will be used to create groups
in the data. A group is created for each unique
combination of the levels of the factor variables in the
list.groups even if
there are no observations in the groupgroups for a
variable (if any) for agegroups[age.var]time.intervalsproc.example.data$initial.ages is a vector with 16
elements as follows 0 1 1 2 0 1 1 2 0 1 1 2 0 1 1 2data, see
dipper,edwards.eberhardt,example.data.
The structure of the encounter history and the analysis
depends on the analysis model to some extent. Thus, it is
necessary to process a dataframe with the encounter
history (ch) and a chosen model to define
the relevant values. For example, number of capture
occasions (nocc) is automatically computed based
on the length of the encounter history (ch) in
data; however, this is dependent on the type of
analysis model. For models such as "CJS", "Pradel" and
others, it is simply the length of ch. Whereas,
for "Burnham" and "Barker" models,the encounter history
contains both capture and resight/recovery values so
nocc is one-half the length of ch.
Likewise, the number of time.intervals depends on
the model. For models, such as "CJS", "Pradel" and
others, the number of time.intervals is
nocc-1; whereas, for capture&recovery(resight)
models the number of time.intervals is
nocc. The default time interval is unit time (1)
and if this is adequate, the function will assign the
appropriate length. A processed data frame can only be
analyzed using the model that was specified. The
model value is used by the functions
make.design.data,
add.design.data, and
make.mark.model to define the model
structure as it relates to the data. Thus, if the data
are going to be analysed with different underlying
models, create different processed data sets with the
model name as an extension. For example,
dipper.cjs=process.data(dipper) and
dipper.popan=process.data(dipper,model="POPAN").
This function will report inconsistencies in the lengths
of the capture history values and when invalid entries
are given in the capture history. For example, with the
"CJS" model, the capture history should only contain 0
and 1 whereas for "Barker" it can contain 0,1,2. For
"Multistrata" models, the code will automatically
identify the number of strata and strata labels based on
the unique alphabetic codes used in the capture
histories.
The argument begin.time specifies the time for the
first capture occasion. This is used in creating the
levels of the time factor variable in the design data and
for labelling parameters. If the begin.time varies
by group, enter a vector of times with one for each
group. Note that the time values for survivals are based
on the beginning of the survival interval and capture
probabilities are labeled based on the time of the
capture occasion. Likewise, age labels for survival are
the ages at the beginning times of the intervals and for
capture probabilities it is the age at the time of
capture/recapture.
groups is a vector of variable names that are
contained in data. Each must be a factor
variable. A group is created for each unique combination
of the levels of the factor variables. In the first
example given below
groups=c("sex","age","region"). which creates
groups defined by the levels of sex, age
and region. There should be
2(sexes)*3(ages)*4(regions)=24 groups but in actuality
there are only 16 in the data because there are only 2
age groups for each sex. Age group 1 and 2 for M and age
groups 2 and 3 for F. This was done to demonstrate that
the code will only use groups that have 1 or more capture
histories unless allgroups=TRUE.
The argument age.var=2 specifies that the second
grouping variable in groups represents an age
variable. It could have been named something different
than age. If a variable in groups is named
age then it is not necessary to specify
age.var. initial.age specifies that the age
at first capture of the age levels is 0,1 and 2 while the
age classes were designated as 1,2,3. The actual ages for
the age classes do not have to be sequential or ordered,
but ordering will cause less confusion. Thus levels
1,2,3 could represent initial ages of 0,4,6 or 6,0,4. The
argument age.unit is the amount an animal ages for each
unit of time and the default is 1. The default for
initial.age is 0 for each group, in which case,
age represents time since marking (first capture)
rather than the actual age of the animal.import.chdata, dipper,
edwards.eberhardt,
example.datadata(example.data)
proc.example.data=process.data(data=example.data,begin.time=1980,
groups=c("sex","age","region"),
age.var=2,initial.age=c(0,1,2))
data(dipper)
dipper.process=process.data(dipper)Run the code above in your browser using DataLab