Bayesian logit model true prevalence for panel — bayesian_panel_logit

The beta distribution priors in this model will actually be converted to logit_normal distributions

Usage

bayesian_panel_logit_model(
  panel_pos_obs,
  panel_n_obs,
  pos_obs,
  n_obs,
  test_names,
  false_pos_controls = NULL,
  n_controls = NULL,
  false_neg_diseased = NULL,
  n_diseased = NULL,
  ...,
  sens = sens_prior(),
  spec = spec_prior(),
  panel_sens = sens_prior(),
  panel_spec = spec_prior(),
  panel_name = "Panel",
  confint = 0.95,
  fmt = "%1.2f%% [%1.2f%% — %1.2f%%]",
  chains = 4,
  warmup = 1000,
  iter = 2000,
  cache_result = TRUE
)

Arguments

panel_pos_obs: the number of positive observations for a given panel of tests
panel_n_obs: the number of observations for each component test
pos_obs: the number of positive observations for a given test
n_obs: the number of observations for a given test
test_names: a vector of the component test names in desired order
false_pos_controls: the number of positives that appeared in the specificity disease-free control group. These are by definition false positives. This is (1-specificity)*n_controls
n_controls: the number of controls in the specificity disease-free control group.
false_neg_diseased: the number of negatives that appeared in the sensitivity confirmed disease group. These are by definition false negatives. This is (1-sensitivity)*n_diseased
n_diseased: the number of confirmed disease cases in the sensitivity control group.
...: not used
sens: the prior sensitivity of the test as a beta_dist.
spec: the prior specificity of the test as a beta_dist.
panel_sens: the prior sensitivity of the panel as a beta_dist (optional)
panel_spec: the prior specificity of the panel as a beta_dist (optional)
panel_name: the name of the panel for combined result
confint: confidence interval limits
fmt: a sprintf formatting string accepting 3 numbers
chains: A positive integer specifying the number of Markov chains. The default is 4.
warmup: A positive integer specifying the number of warmup (aka burnin) iterations per chain. If step-size adaptation is on (which it is by default), this also controls the number of iterations for which adaptation is run (and hence these warmup samples should not be used for inference). The number of warmup iterations should be smaller than iter and the default is iter/2.
iter: A positive integer specifying the number of iterations for each chain (including warmup). The default is 2000.
cache_result: save the result of the sampling in memory for the current session

Value

a list of dataframes containing the prevalence, sensitivity, and specificity estimates, and a stanfit object with the raw fit data