new_epiparameter | R Documentation |
<epiparameter>
classCreate an <epiparameter>
object. The
constructor will search whether parameters of the probability distribution
are supplied and if not look to see whether they can be inferred/extracted/
converted from summary statistics provided. It will also convert the
probability distribution (prob_dist
) and its parameters
(prob_dist_params
) into an S3 class, either a distribution
object from
{distributional}
when discretise = FALSE
, or a distcrete
object from
{distcrete}
when discretise = TRUE
.
new_epiparameter(
disease = character(),
pathogen = character(),
epi_name = character(),
prob_distribution = list(),
uncertainty = list(),
summary_stats = list(),
citation = character(),
metadata = list(),
method_assess = list(),
notes = character(),
auto_calc_params = logical(),
...
)
disease |
A |
pathogen |
A |
epi_name |
A |
prob_distribution |
An S3 class containing the probability
distribution or a character string if the parameters of the probability
distribution are unknown but the name of the distribution is known, or |
uncertainty |
A list of named vectors with the uncertainty around
the probability distribution parameters. If uncertainty around the parameter
estimates is unknown use |
summary_stats |
A list of summary statistics, use
|
citation |
A |
metadata |
A list of metadata, this can include: units, sample size,
the transmission mode of the disease (e.g. is it vector-borne or directly
transmitted), etc. It is assumed that the disease is not
vector-borne and that the distribution is intrinsic (e.g. not an extrinsic
delay distribution such as extrinsic incubation period) unless
|
method_assess |
A list of methodological aspects used when fitting
the distribution, use |
notes |
A |
auto_calc_params |
A boolean |
... |
dots Extra arguments to be passed to internal functions. This is most commonly used to pass arguments to |
An <epiparameter>
object.
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