parameter_set: Parameter set helper functions
In dynverse/dynparam: Creating Meta-Information for Parameters

Description Usage Arguments Parameter set instatiations Serialisation See Also Examples

Parameter set helper functions

parameter_set(..., parameters = NULL, forbidden = NULL)

is_parameter_set(x)

## S3 method for class 'parameter_set'
as.list(x, ...)

as_parameter_set(li)

get_defaults(x)

sip(x, n = 1, as_tibble = TRUE)

as_paramhelper(x)

`...`	Parameters to wrap in a parameter set.
`parameters`	A list of parameters to wrap in a parameter set.
`forbidden`	States forbidden region of parameter via a character vector, which will be turned into an expression.
`x`	An object for which to check whether it is a parameter set.
`li`	A list to be converted into a parameter set.
`n`	Number of objects to return.
`as_tibble`	Whether or not to return as a tibble.

get_defaults(): Get all default parameters.
sip(): It's like sample(), but for parameter sets.
as_paramhelper(): Convert a parameter set to a ParamHelpers object.

as.list(): Converting a parameter set to a list.
as_parameter_set(): Converting a list back to a parameter set.
is_parameter_set(x): Checking whether something is a parameter set.

dynparam for an overview of all dynparam functionality.

parameters <- parameter_set(
  integer_parameter(
    id = "num_iter",
    default = 100L,
    distribution = expuniform_distribution(lower = 1L, upper = 10000L),
    description = "Number of iterations"
  ),
  subset_parameter(
    id = "dimreds",
    default = c("pca", "mds"),
    values = c("pca", "mds", "tsne", "umap", "ica"),
    description = "Which dimensionality reduction methods to apply (can be multiple)"
  ),
  integer_range_parameter(
    id = "ks",
    default = c(3L, 15L),
    lower_distribution = uniform_distribution(1L, 5L),
    upper_distribution = uniform_distribution(10L, 20L),
    description = "The numbers of clusters to be evaluated"
  )
)

get_defaults(parameters)

sip(parameters, n = 1)