Create comprehensive visualizations for power analysis results from rctbp_power_analysis objects. Supports different plot types based on analysis type (sample_only, effect_only, or both varying).
Arguments
- x
An rctbp_power_analysis object that has been run with results
- type
Type of plot to create:
"auto" - Automatically detect best plot type based on analysis (default)
"power_curve" - Power curve across single varying dimension
"heatmap" - 2D heatmap when both sample sizes and effect sizes vary
"integrated" - Integrated power results when design prior is used
"comparison" - Compare power vs posterior probabilities
- metric
Which power metric to display:
"success" - Success power and probability
"futility" - Futility power and probability
"both" - Both success and futility power and probabilities (default)
- values
Which values to display:
"both" - Both power and posterior probabilities (default)
"power" - Power only
"post_prob" - Posterior probabilities only
- show_target
Whether to show target power lines (default: TRUE)
- show_integrated
Whether to include integrated power when available (default: TRUE)
- facet_by
For power_curve plots when both sample sizes and effect sizes vary:
"effect_size" - Facet by effect size, vary sample size on x-axis (default)
"sample_size" - Facet by sample size, vary effect size on x-axis
- design_prior
Optional design prior for runtime integrated power computation. Can be:
A string in brms prior syntax (e.g., "normal(0.3, 0.1)", "student_t(6, 0.5, 0.2)")
An R function taking effect size as input (e.g., function(x) dnorm(x, 0.5, 0.2))
NULL for no runtime integration (default)
If provided, integrated power will be computed using this design prior instead of any design prior specified in the original rctbp_power_analysis object. Only valid when effect sizes vary (length > 1).
- ...
Additional arguments passed to plotly functions