Set up a new distribution for a data type or a process
Description
Use methods::new() to set up a distribution within an existing module with
the necessary linkages between the two. For example, a fleet module will need
a distributional assumption for parts of the data associated with it, which
requires the use of initialize_data_distribution(), and a recruitment
module, like the Beverton–Holt stock–recruit relationship, will need a
distribution associated with the recruitment deviations, which requires
initialize_process_distribution().
Usage
initialize_data_distribution(
module,
family,
sd = list(value = 1, estimated = FALSE),
data_type = c("index", "agecomp", "lengthcomp")
)
initialize_process_distribution(
module,
par,
family,
sd = list(value = 1, estimated = FALSE),
is_random_effect = FALSE
)
initialize_data_distribution(
module,
family,
sd = list(value = 1, estimated = FALSE),
data_type = c("index", "agecomp", "lengthcomp")
)
initialize_process_distribution(
module,
par,
family,
sd = list(value = 1, estimated = FALSE),
is_random_effect = FALSE
)
Arguments
module |
An identifier to a C++ fleet module that is linked to the data
of interest.
|
family |
A description of the error distribution and link function to
be used in the model. The argument takes a family class, e.g.,
stats::gaussian(link = "identity").
|
sd |
A list of length two. The first entry is named "value" and it
stores the initial values (scalar or vector) for the relevant standard
deviations. The default is value = 1. The second entry is named
"estimated" and it stores a vector of booleans (default = FALSE) is a
scalar indicating whether or not standard deviation is estimated. If
"value" is a vector and "estimated" is a scalar, the single value
specified "estimated" value will be repeated to match the length of
value. Otherwise, the dimensions of the two must match.
|
data_type |
A string specifying the type of data that the
distribution will be fit to. Allowable types include
c, index, agecomp, lengthcomp
and the default is
c.
|
par |
A string specifying the parameter name the distribution applies
to. Parameters must be members of the specified module. Use
methods::show(module) to obtain names of parameters within the module.
|
is_random_effect |
A boolean indicating whether or not the process is
estimated as a random effect.
|
Value
A reference class. is returned. Use methods::show() to view the various
Rcpp class fields, methods, and documentation.
Examples
## Not run:
# Set up a new data distribution
n_years <- 30
# Create a new fleet module
fleet <- methods::new(Fleet)
# Create a distribution for the fleet module
fleet_distribution <- initialize_data_distribution(
module = fishing_fleet,
family = lognormal(link = "log"),
sd = list(
value = rep(sqrt(log(0.01^2 + 1)), n_years),
estimated = rep(FALSE, n_years) # Could also be a single FALSE
),
data_type = "index"
)
# Set up a new process distribution
# Create a new recruitment module
recruitment <- methods::new(BevertonHoltRecruitment)
# view parameter names of the recruitment module
methods::show(BevertonHoltRecruitment)
# Create a distribution for the recruitment module
recruitment_distribution <- initialize_process_distribution(
module = recruitment,
par = "log_devs",
family = gaussian(),
sd = list(value = 0.4, estimated = FALSE),
is_random_effect = FALSE
)
## End(Not run)
n_years <- 30
fleet <- methods::new(Fleet)
fleet_distribution <- initialize_data_distribution(
module = fishing_fleet,
family = lognormal(link = "log"),
sd = list(
value = rep(sqrt(log(0.01^2 + 1)), n_years),
estimated = rep(FALSE, n_years)
),
data_type = "index"
)
recruitment <- methods::new(BevertonHoltRecruitment)
methods::show(BevertonHoltRecruitment)
recruitment_distribution <- initialize_process_distribution(
module = recruitment,
par = "log_devs",
family = gaussian(),
sd = list(value = 0.4, estimated = FALSE),
is_random_effect = FALSE
)
,
Jon K. T. Brodziak [aut]