Prior to analyzing the data, this function initializes several variables (e.g., number of capture occasions, time intervals) that are often specific to the capture-recapture model being fitted to the data. It also is used to 1) define groups in the data that represent different levels of one or more factor covariates (e.g., sex), 2) define time intervals between capture occasions (if not 1), and 3) create an age structure for the data, if any.
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,
areas = NULL,
events = NULL
)processed.data (a list with the following elements)
original raw dataframe with group factor variable added if groups were defined
type of analysis model (eg, "CJS", "Burnham", "Barker")
a dataframe of frequencies (same number of rows as data, number of columns is the number of groups in the data. The column names are the group labels representing the unique groups that have one or more capture histories.
number of capture occasions
length of time intervals between capture occasions
time of first capture occasion
increment of age for each increment of time
an initial age for
each group in the data; Note that this is not the original argument but is a
vector with the initial age for each group. In the first example below
proc.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 2
number of strata in Multistrata models
vector of alphabetic characters used to identify strata in Multistrata models
factor covariates used to define groups
A data frame with at least one field named 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 default structure is freq=1 and it need not be included
in the dataframe. data can also contain an arbitrary number of
covariates specific to animals with that capture history.
Time of first capture occasion or vector of times if different for each group
Type of analysis model. See mark for a list of
possible values for model
Number of mixtures in closed capture models with heterogeneity or number of secondary samples for MultScalOcc/RDMultScalOcc model
Vector of factor variable names (in double quotes) in
data 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.
Logical variable; if TRUE, all groups are created from
factors defined in groups even if there are no observations in the
group
An index in vector groups for a variable (if any) for
age
A vector of initial ages that contains a value for each
level of the age variable groups[age.var]
Increment of age for each increment of time as defined by
time.intervals
Vector of lengths of time between capture occasions
number of occasions for Nest type; either nocc or time.intervals must be specified
vector of single character values used in capture history(ch) for ORDMS, CRDMS, RDMSOccRepro, HidMarkov models; it can contain more values beyond what is in ch for unobservable states except for RDMSOccRepro which is used to specify strata ordering (eg 0 not-occupied, 1 occupied no repro, 2 occupied with repro.
named list of numeric vectors (one group) or matrices (>1 group) containing counts for mark-resight models
if set to TRUE, will reverse timing of transition (Psi) and survival (S) in Multistratum models
values of areas (1 per group) for Densitypc set of models
vector of character events for Hidden Markov models
Jeff Laake
For examples of data, 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.data
data(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)
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