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 history. The demark 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