# bayes_sampsize: Determine the required sample size for a Bayesian hypothesis... In BayesianPower: Sample Size and Power for Comparing Inequality Constrained Hypotheses

## Description

Determine the required sample size for a Bayesian hypothesis test

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```bayes_sampsize( h1, h2, m1, m2, sd1 = 1, sd2 = 1, scale = 1000, type = 1, cutoff, bound1 = 1, bound2 = 1/bound1, datasets = 1000, nsamp = 1000, minss = 2, maxss = 1000, seed = 31 ) ```

## Arguments

 `h1` A constraint matrix defining H1. `h2` A constraint matrix defining H2. `m1` A vector of expected population means under H1 (standardized). `m2` A vector of expected populations means under H2 (standardized). `m2` must be of same length as `m1` `sd1` A vector of standard deviations under H1. Must be a single number (equal standard deviation under all populations), or a vector of the same length as `m1` `sd2` A vector of standard deviations under H2. Must be a single number (equal standard deviation under all populations), or a vector of the same length as `m2` `scale` A number specifying the prior scale `type` A character. The type of error to be controlled options are: `"1", "2", "de", "aoi", "med.1", "med.2"` `cutoff` A number. The cutoff criterion for type. If `type` is `"1", "2", "de", "aoi"`, `cutoff` must be between 0 and 1 If `type` is `"med.1" or "med.2"`, `cutoff` must be larger than 1 `bound1` A number. The boundary above which BF12 favors H1 `bound2` A number. The boundary below which BF12 favors H2 `datasets` A number. The number of datasets to compute the error probabilities `nsamp` A number. The number of prior or posterior samples to determine the fit and complexity `minss` A number. The minimum sample size to consider `maxss` A number. The maximum sample size to consider `seed` A number. The random seed to be set

## Value

The sample size for which the chosen type of error probability is at the set cutoff, and the according error probabilities and median Bayes factors

## Examples

 ```1 2 3 4 5 6 7 8``` ```# Short computation example NOT SUFFICIENT SAMPLES h1 <- matrix(c(1,-1), nrow= 1, byrow= TRUE) h2 <- 'c' m1 <- c(.4, 0) m2 <- c(0, .1) bayes_sampsize(h1, h2, m1, m2, sd1 = 1, sd2 = 1, scale = 1000, type = "de", cutoff = .125, nsamp = 50, datasets = 50, minss = 40, maxss = 70) ```

BayesianPower documentation built on July 1, 2020, 6:02 p.m.