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Distributions

Source: R/distributions.R
distribution.Rd

Distributions which can be used to specify prior or likelihood components in model calibration.

  • Uniform Distribution (Improper), only appropriate for prior components - mp_uniform

  • Normal Distribution - mp_normal

  • Log-Normal Distribution - mp_log_normal

  • Logit-Normal Distribution - mp_logit_normal

  • Poisson Distribution - mp_poisson

  • Negative Binomial Distribution - mp_neg_bin

Usage

mp_uniform(trans_distr_param = list())

mp_normal(
  location = mp_distr_param_null("location"),
  sd,
  trans_distr_param = list(location = mp_identity, sd = mp_log)
)

mp_log_normal(
  location = mp_distr_param_null("location"),
  sd,
  trans_distr_param = list(location = mp_identity, sd = mp_identity)
)

mp_logit_normal(
  location = mp_distr_param_null("location"),
  sd,
  trans_distr_param = list(location = mp_identity, sd = mp_identity)
)

mp_poisson(
  location = mp_distr_param_null("location"),
  trans_distr_param = list(location = mp_identity)
)

mp_neg_bin(
  location = mp_distr_param_null("location"),
  disp,
  trans_distr_param = list(location = mp_identity, disp = mp_log)
)

Arguments

trans_distr_param

Named list of transformations for each distributional parameter. See transform_distr_param for a list of available transformations.

location

Location parameter. Specifying the location parameter is only necessary when the distribution is used as a prior distribution. If it is used as a likelihood component the location parameter will be taken as the simulated variable being fitted to data, and so this location parameter should be left to the default.

sd

Standard deviation parameter.

disp

Dispersion parameter.

Details

All distributional parameter arguments can be specified either as a numeric value, a character string giving the parameter name, or a distributional parameter object (See fit_distr_params).