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. Also see
merge_design.covariates to merge data like environmental variables
or any other data that is common to all animals or group/time specific values.
add.design.data(
data,
ddl,
parameter,
type = "age",
bins = NULL,
name = NULL,
replace = FALSE,
right = TRUE
)Design data list with new field added for the specified parameter.
See make.design.data for a description of the list structure.
processed data list resulting from process.data
current design dataframe initially created with
make.design.data
name of model parameter (e.g., "Phi" for CJS models)
either "age", "time" or "cohort"
bins for grouping
name assigned to variable in design data
if TRUE, replace any variable with same name as name
If TRUE, bin intervals are closed on the right
Jeff Laake
Design data can be added to the parameter specific design dataframes with R
commands and this function does NOT have to be used to add design data. However,
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 parsimonious 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:
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:
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 pre-defined 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 re-defined to use 0-1 and 2+ with the command:
ddl=add.design.data(proc.example.data,ddl,parameter="Phi",type="age",
bins=c(0,1,10),name="2ages",replace=TRUE) 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.
make.design.data, process.data
# \donttest{
# 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)
# }
Run the code above in your browser using DataLab