dot-get_prior_parameters: Get priors for Bayesian DCA
In giulianonetto/bayesdca: Bayesian Decision Curve Analysis

.get_prior_parameters

R Documentation

Get priors for Bayesian DCA

Description

Get priors for Bayesian DCA

Usage

.get_prior_parameters(
  thresholds,
  threshold_varying_prior = FALSE,
  n_strategies = NULL,
  prior_p = NULL,
  prior_se = NULL,
  prior_sp = NULL,
  ignorance_region_cutpoints = c(0.25, 0.75) * max(thresholds),
  min_sens_prior_mean = 0.01,
  max_sens_prior_mean = 0.99,
  max_sens_prior_sample_size = 5,
  ignorance_region_mean = 0.5,
  ignorance_region_sample_size = 2,
  prev_prior_mean = 0.5,
  prev_prior_sample_size = 2
)

Arguments

`thresholds`	Vector of decision thresholds.
`shift`	Scalar controlling height of prior Specificity curve. Only used if `constant=FALSE`.
`slope`	Scalar controlling shape of prior Specificity curve. Only used if `constant=FALSE`.
`min_mean_se, min_mean_sp, max_mean_se, max_mean_se`	Minimum
`prior_sample_size`	Prior sample size of strength.
`min_prior_sample_size`	Minimum prior sample size or strength.
`max_prior_sample_size`	Maximum prior sample size or strength.
`slope_prior_sample_size`	Rate of change in prior sample size or strength. and maximum prior mean for sensitivity (se) and specificity (sp).