Description Usage Arguments Details Value Note Author(s) See Also Examples
View source: R/add.design.data.R
Creates new design data fields in (ddl
) that bin the fields
cohort
, age
or time
. Other fields (e.g., effort value
for time) can be added to ddl
with R commands.
1 2 
data 
processed data list resulting from 
ddl 
current design dataframe initially created with

parameter 
name of model parameter (e.g., "Phi" for CJS models) 
type 
either "age", "time" or "cohort" 
bins 
bins for grouping 
name 
name assigned to variable in design data 
replace 
if TRUE, replace any variable with same name as 
right 
If TRUE, bin intervals are closed on the right 
Design data can be added to the parameter specific design dataframes with R
commands. Often the additional fields will be functions of cohort
,
age
or time
. add.design.data
provides an easy way to
add fields that bin (put into intervals) the original values of
cohort
, age
or time
. For example, age
may have
levels from 0 to 10 which means the formula ~age
will have 11
parameters, one for each level of the factor. It might be more desirable
and more parimonious to have a simpler 2 age class model of young and
adults. This can be done easily by adding a new design data field that bins
age
into 2 intervals (age 0 and 1+) as in the following example:
1 2 3  ddl=make.design.data(proc.example.data)
ddl=add.design.data(proc.example.data,ddl,parameter="Phi",type="age",
bins=c(0,.5,10),name="2ages")

By default, the bins are open on the left and closed on the right (i.e.,
binning x by (x1,x2] is equivalent to x1<x<=x2) except for the first
interval which is closed on the left. Thus, for the above example, the age
bins are [0,.5] and (.5,10]. Since the ages in the example are 0,1,2...
using any value >0 and <1 in place of 0.5 would bin the ages into 2 classes
of 0 and 1+. This behavior can be modified by changing the argument
right=FALSE to create an interval that is closed on the left and open on the
right. In some cases this can make reading the values of the levels
somewhat easier. It is important to recognize that the new variable is only
added to the design data for the defined parameter
and can only be
used in model formula for that parameter. Multiple calls to
add.design.data
can be used to add the same or different fields for
the various parameters in the model. For example, the same 2 age class
variable can be added to the design data for p with the command:
1 2  ddl=add.design.data(proc.example.data,ddl,parameter="p",type="age",
bins=c(0,.5,10),name="2ages")

The name
must be unique within the parameter design data, so they
should not use predefined values of group, age, Age, time, Time,
cohort, Cohort
. If you choose a name
that already exists in the
design data for the parameter
, it will not be added but it can
replace the variable if replace=TRUE
. For example, the 2ages
variable can be redefined to use 01 and 2+ with the command:
1 2 
Keep in mind that design data are stored with the mark
model object
so if a variable is redefined, as above, this could become confusing if some
models have already been constructed using a different definition for the
variable. The model formula and names would appear to be identical but they
would have a different model structure. The difference would be apparent if
you examined the design data and design matrix of the model object but would
the difference would be transparent based on the model names and formula.
Thus, it would be best to avoid constructing models from design data fields
with different structures but the same name.
Design data list with new field added for the specified parameter.
See make.design.data
for a description of the list structure.
For the specific case of "closed" capture models, the parameters
p
(capture probability) and c
(recapture probability) can be
treated in a special fashion. Because they really the same type of
parameter, it is useful to be able to share a common model structure (i.e.,
same columns in the design matrix). This is indicated with the
share=TRUE
element in the model description for p
. If the
parameters are shared then the additional covariate c
is added to the
design data, which is c=0
for parameter p
and c=1
for
parameter c
. This enables an additive model to be developed where
recapture probabilities mimic the pattern in capture probabilities except
for an additive constant. The covariate c
can only be used in the
model for p
if share=TRUE
. If the latter is not set using
c
in a formula will result in an error. Likewise, if
share=TRUE
, then the design data for p
and c
must be
the same because the design data are merged in constructing the design
matrix. Thus if you add design data for parameter p
, you should add
a similar field for parameter c
if you intend to fit shared models
for the two parameters. If the design data do not match and you try to fit
a shared model, an error will result.
Jeff Laake
make.design.data
, process.data
1 2 3 4 5 6 7 8 9 10  # This example is excluded from testing to reduce package check time
data(example.data)
example.data.proc=process.data(example.data)
ddl=make.design.data(example.data.proc)
ddl=add.design.data(example.data.proc,ddl,parameter="Phi",type="age",
bins=c(0,.5,10),name="2ages")
ddl=add.design.data(example.data.proc,ddl,parameter="p",type="age",
bins=c(0,.5,10),name="2ages")
ddl=add.design.data(example.data.proc,ddl,parameter="Phi",type="age",
bins=c(0,1,10),name="2ages",replace=TRUE)

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