Bayesian confidence intervals (credible intervals) as alternative to frequentist confidence intervals. This analysis provides credible intervals using Beta-posterior distributions, Monte Carlo sampling, and various prior specifications for continuous and binary outcome data. Essential for Bayesian statistical inference in clinical research.
Usage
bayesianci(
data,
outcome,
group_var,
covariates,
outcome_type = "continuous",
credible_level = 0.95,
additional_levels = "0.50,0.80,0.90",
prior_type = "uniform",
beta_alpha = 1,
beta_beta = 1,
normal_mean = 0,
normal_sd = 1,
gamma_shape = 1,
gamma_rate = 1,
mcmc_method = "analytical",
mcmc_samples = 10000,
burnin_samples = 2000,
thinning = 1,
chains = 3,
posterior_summary = TRUE,
convergence_diagnostics = TRUE,
prior_posterior_comparison = TRUE,
credible_vs_confidence = TRUE,
hpd_intervals = TRUE,
sensitivity_analysis = TRUE,
robustness_check = TRUE,
clinical_interpretation = TRUE,
posterior_plots = TRUE,
trace_plots = TRUE
)Arguments
- data
the data as a data frame
- outcome
Outcome variable for credible interval estimation
- group_var
Grouping variable for stratified analysis
- covariates
Covariate variables for adjusted analysis
- outcome_type
Type of outcome variable for appropriate modeling
- credible_level
Level for Bayesian credible intervals
- additional_levels
Comma-separated additional credible levels to compute
- prior_type
Prior distribution specification for Bayesian analysis
- beta_alpha
Alpha parameter for Beta prior distribution
- beta_beta
Beta parameter for Beta prior distribution
- normal_mean
Mean parameter for Normal prior distribution
- normal_sd
Standard deviation for Normal prior distribution
- gamma_shape
Shape parameter for Gamma prior distribution
- gamma_rate
Rate parameter for Gamma prior distribution
- mcmc_method
Method for posterior sampling and credible interval computation
- mcmc_samples
Number of MCMC samples for posterior estimation
- burnin_samples
Number of burn-in samples to discard
- thinning
Thinning interval for MCMC chains
- chains
Number of parallel MCMC chains
- posterior_summary
Compute posterior mean, median, mode, and credible intervals
- convergence_diagnostics
Assess MCMC convergence using R-hat and effective sample size
- prior_posterior_comparison
Compare prior and posterior distributions
- credible_vs_confidence
Compare Bayesian credible intervals with frequentist confidence intervals
- hpd_intervals
Compute highest posterior density (HPD) credible intervals
- sensitivity_analysis
Assess sensitivity to prior specification
- robustness_check
Perform robustness checks with different prior specifications
- clinical_interpretation
Provide clinical interpretation of credible intervals
- posterior_plots
Create posterior distribution and credible interval visualizations
- trace_plots
Generate MCMC trace plots for convergence assessment
Value
A results object containing:
results$instructions | a html | ||||
results$dataInfo | a table | ||||
results$priorSpecification | a table | ||||
results$posteriorSummary | a table | ||||
results$credibleIntervals | a table | ||||
results$hpdIntervals | a table | ||||
results$comparisonTable | a table | ||||
results$convergenceDiagnostics | a table | ||||
results$sensitivityAnalysis | a table | ||||
results$robustnessCheck | a table | ||||
results$clinicalInterpretation | a table | ||||
results$posteriorPlot | an image | ||||
results$priorPosteriorPlot | an image | ||||
results$tracePlots | an image | ||||
results$credibleComparisonPlot | an image | ||||
results$sensitivityPlot | an image | ||||
results$methodExplanation | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$dataInfo$asDF
as.data.frame(results$dataInfo)