plot_robustness: Plot Bayes Factor Robustness Check In abtest: Bayesian A/B Testing

Description

Function for plotting Bayes factor robustness check results (i.e., prior sensitivity analysis).

Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```plot_robustness( x, bftype = "BF10", log = FALSE, mu_range = c(0, 0.3), sigma_range = c(0.25, 1), mu_steps = 40, sigma_steps = 40, cores = 1, ... ) ```

Arguments

 `x` object of class `"ab"`. `bftype` character that specifies which Bayes factor is plotted. Either `"BF10"`, `"BF01"`, `"BF+0"`, `"BF0+"`, `"BF-0"`, or `"BF0-"`. `log` Boolean that specifies whether the log Bayes factor is plotted. `mu_range` numeric vector of length two that specifies the range of `mu_psi` values to consider. `sigma_range` numeric vector of length two that specifies the range of `sigma_psi` values to consider. `mu_steps` numeric value that specifies in how many discrete steps the interval `mu_range` is partitioned. `sigma_steps` numeric value that specifies in how many discrete steps the interval `sigma_range` is partitioned. `cores` number of cores used for the computations. `...` further arguments passed to `filled.contour`.

Details

The plot shows how the Bayes factor changes as a function of the normal prior location parameter `mu_psi` and the normal prior scale parameter `sigma_psi` (i.e., a prior sensitivity analysis with respect to the normal prior on the test-relevant log odds ratio).

Value

Returns a `data.frame` with the `mu_psi` values, `sigma_psi` values, and corresponding (log) Bayes factors.

Author(s)

Quentin F. Gronau

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## Not run: # synthetic data data <- list(y1 = 10, n1 = 28, y2 = 14, n2 = 26) # Bayesian A/B test with default settings ab <- ab_test(data = data) # plot robustness check (i.e., prior sensitivity analysis) p <- plot_robustness(ab) # returned object contains the Bayes factors for the different prior settings head(p) ## End(Not run) ```

abtest documentation built on Nov. 22, 2021, 9:07 a.m.