R: Simulate from Parameter Priors

simulate_priors {abtest} | R Documentation

## Simulate from Parameter Priors

### Description

Function for simulating from the parameter prior distributions.

### Usage

    simulate_priors(
      nsamples,
      prior_par = list(mu_psi = 0, sigma_psi = 1, mu_beta = 0, sigma_beta = 1),
      hypothesis = "H1"
    )

### Arguments

`nsamples` | number of samples.
`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.
`hypothesis` | character specifying whether to sample from 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-").

### Value

a data frame with prior samples for the following quantities (see `?ab_test` for a description of the underlying model):

- `beta`: prior samples for the grand mean of the log odds.

- `psi`: prior samples for the log odds ratio.

- `p1`: prior samples for the latent "success" probability in the control group.

- `p2`: prior samples for the latent "success" probability in the experimental group.

- `logor`: prior samples for the log odds ratio (identical to `psi`, only included for easier reference).

- `or`: prior samples for the odds ratio.

- `rrisk`: prior samples for the relative risk (i.e., the ratio of the "success" probability in the experimental and the control condition).

- `arisk`: prior samples for the absolute risk (i.e., the difference of the "success" probability in the experimental and control condition)

.

### Author(s)

Quentin F. Gronau

### Examples

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

    # obtain prior samples
    samples <- simulate_priors(nsamples = 1000, prior_par = prior_par)

    # plot, e.g., prior samples for absolute risk
    hist(samples$arisk)

[Package abtest version 1.0.1 Index]