Function reference • testerror

All functions

apparent_prevalence(): Apparent prevalence from known prevalence

as_tibble(<beta_dist>): convert a beta distribution to a tibble

as_tibble(<beta_dist_list>): convert a list of betas to a tibble

bayesian_component_logit_model(): Bayesian simpler model true prevalence for component

bayesian_component_simpler_model(): Bayesian simpler model true prevalence for component

bayesian_panel_complex_model(): Bayesian models true prevalence for panel

bayesian_panel_logit_model(): Bayesian logit model true prevalence for panel

bayesian_panel_simpler_model(): Bayesian simpler model true prevalence for panel

bayesian_panel_true_prevalence_model(): Execute one of a set of bayesian models

bayesian_true_prevalence_model(): Execute one of a set of bayesian models

beta_dist(): Generate a beta distribution out of probabilities, or positive and negative counts

beta_fit(): Fit a beta distribution to data using method of moments

beta_params(): Generate concave beta distribution parameters from mean and confidence intervals

ci_to_logitnorm(): Generate mu and sigma parameters for a logitnormal distribution

.input_data: Dataframe format for component test results

.input_panel_data: Dataframe format for panel test results

.output_data: Dataframe format for true prevalence results

format(<beta_dist>): Format a beta distribution

format(<beta_dist_list>): Format a beta distribution list

fp_p_value(): Significance of an uncertain test result

fp_signif_level(): Identify the minimum number of positive test result observations needed to be confident the disease has a non-zero prevalence.

get_beta_shape(): Get a parameter of the beta_dist

get_beta_shape(<beta_dist>): Get a parameter of the beta_dist

get_beta_shape(<beta_dist_list>): Get a parameter of the beta_dist

inv_logit(): The inverse logit function

length(<beta_dist>): Detect the length of a beta distribution

length(<beta_dist_list>): Detect the length of a beta distribution list

logit(): The logit function

odds_ratio_ve() odds_ratio_ve(): Calculate a vaccine effectiveness estimate based on an odds ratio

optimal_performance(): Test optimal performance

panel_prevalence(): Expected test panel prevalence assuming independence

panel_sens(): Test panel combination sensitivity

panel_sens_estimator(): Estimate test panel combination sensitivity

panel_spec(): Test panel combination specificity

prevalence_lang_reiczigel(): True prevalence from apparent prevalence with uncertainty

prevalence_panel_lang_reiczigel(): Lang-Reiczigel true prevalence for panel

print(<beta_dist>): Print a beta distribution

print(<beta_dist_list>): Print a beta distribution

relative_risk_ve(): Calculate a vaccine effectiveness estimate based on a risk ratio

rep(<beta_dist>): Repeat a beta_dist

rogan_gladen(): True prevalence from apparent prevalence

sens_prior(): The default prior for specificity

spec_prior(): The default prior for specificity

true_panel_prevalence(): Calculate an estimate of true prevalence for a single panel and components

true_prevalence(): Vectorised true prevalence estimates

uncertain_panel_rogan_gladen(): Rogan-Gladen true prevalence for panel with resampling

uncertain_panel_sens_estimator(): Propagate component test sensitivity and specificity into panel specificity assuming a known set of observations of component apparent prevalence

uncertain_panel_spec(): Propagate component test specificity into panel specificity

uncertain_rogan_gladen(): True prevalence from apparent prevalence with uncertainty

underestimation_threshold(): Test underestimation limit

uniform_prior(): A uniform prior

uninformed_prior(): Uninformative prior

update_posterior(): Update the posterior of a beta_dist

update_posterior(<beta_dist>): Update the posterior of a beta_dist

update_posterior(<beta_dist_list>): Update the posterior of a beta_dist