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abc_prior S3 class

Source: R/s3-abc_prior-class.R
abc_prior.Rd

abc_prior S3 class

Usage

new_abc_prior(.dists, .constraints = list(), .derived = list(), .cor = NULL)

as.abc_prior(x, ...)

is.abc_prior(x, ...)

# S3 method for class 'abc_prior'
format(x, ...)

# S3 method for class 'abc_prior'
print(x, ...)

# S3 method for class 'abc_prior'
plot(x, ...)

Arguments

.dists

distribution functions as a named list of S3 dist_fns objects

.constraints

a list of one sided formulae the result each of which should evaluate to a boolean when compared against the names of the priors and derived values.

.derived

a list of two sided formulae. The RHS refer to the priors, and the LHS as a name to derive.

.cor

(optional) a correlation matrix for the priors

x

an abc_prior S3 object

...

passed on to methods

Value

an S3 object of class abc_prior which contains

  • a list of dist_fns

  • a cor attribute describing their correlation

  • a derived attribute describing derive values

  • a constraints attribute listing the constraints

  • a params attribute listing the names of the parameters

Methods (by generic)

  • format(abc_prior): Format an abc_prior

  • print(abc_prior): Print an abc_prior

  • plot(abc_prior): Plot an abc_prior

Functions

  • new_abc_prior(): Create a new prior

  • as.abc_prior(): Create a prior from a named list of dist_fns

  • is.abc_prior(): Test is an abc_prior

Unit tests



p = new_abc_prior(
  .dists = list(
    mean = as.dist_fns("norm",4,2),
    sd = as.dist_fns("gamma",2)
  ),
  .derived = list(
    shape ~ mean^2 / sd^2,
    rate ~ mean / sd^2
  ),
  .constraints = list(
    ~ mean > sd
  )
)

testthat::expect_equal(
  format(p),
  "Parameters: \n* mean: norm(mean = 4, sd = 2)\n* sd: gamma(shape = 2, rate = 1)\nConstraints:\n* mean > sd\nDerived values:\n* shape = mean^2/sd^2\n* rate = mean/sd^2"
)