plot_prior {abtest}

Plot Prior

Description

Function for plotting parameter prior distributions.

Usage

plot_prior(
  prior_par = list(mu_psi = 0, sigma_psi = 1, mu_beta = 0, sigma_beta = 1),
  what = "logor",
  hypothesis = "H1",
  p1 = 0.5,
  ...
)

Arguments

prior_par

list with prior parameters. This list needs to contain the following elements: mu_psi (prior mean for the normal prior on the test-relevant log odds ratio), sigma_psi (prior standard deviation for the normal prior on the test-relevant log odds ratio), mu_beta (prior mean for the normal prior on the grand mean of the log odds), sigma_beta (prior standard deviation for the normal prior on the grand mean of the log odds). Each of the elements needs to be a real number (the standard deviations need to be positive). The default are standard normal priors for both the log odds ratio parameter and the grand mean of the log odds parameter.

what

character specifying for which quantity the prior should be plotted. Either "logor" (i.e., log odds ratio) , "or" (i.e., odds ratio), "p1p2" (i.e., plots the joint distribution of the latent "success" probability in the experimental and control condition), "p1" (i.e., latent "success" probability in the control condition), "p2" (i.e., latent "success" probability in the experimental condition), "p2givenp1" (i.e., plots the conditional distribution of the latent "success" probability in the experimental condition given a "success" probability of p1 in the control condition), "rrisk" (i.e., relative risk, the ratio of the "success" probability in the experimental and the control condition), or "arisk" (i.e., absolute risk, the difference of the "success" probability in the experimental and control condition).

hypothesis

character specifying whether to plot a two-sided prior (i.e., "H1"), a one-sided prior with lower truncation point (i.e., "H+"), or a one-sided prior with upper truncation point (i.e., "H-").

p1

value of the "success" probability in the control condtion. Only used when what = "p2givenp1".

...

further arguments.

Note

Internally, the test-relevant prior is always a normal prior on the log odds ratio, however, the plot_prior function also allows one to plot the implied prior on different quantities.

Author(s)

Quentin F. Gronau

Examples

# prior parameters
prior_par <- list(mu_psi = 0, sigma_psi = 1,
                  mu_beta = 0, sigma_beta = 1)

# plot prior
plot_prior(prior_par = prior_par, what = "logor")
plot_prior(prior_par = prior_par, what = "or")
plot_prior(prior_par = prior_par, what = "p1p2")
plot_prior(prior_par = prior_par, what = "p1")
plot_prior(prior_par = prior_par, what = "p2")
plot_prior(prior_par = prior_par, what = "p2givenp1", p1 = 0.3)
plot_prior(prior_par = prior_par, what = "rrisk")
plot_prior(prior_par = prior_par, what = "arisk")