BayesianPower (version 0.2.3)

bayes_power: Determine the 'power' for a Bayesian hypothesis test

Description

Determine the 'power' for a Bayesian hypothesis test

Usage

bayes_power(
  n,
  h1,
  h2,
  m1,
  m2,
  sd1 = 1,
  sd2 = 1,
  scale = 1000,
  bound1 = 1,
  bound2 = 1/bound1,
  datasets = 1000,
  nsamp = 1000,
  seed = 31
)

Arguments

n

A number. The sample size

h1

A constraint matrix defining H1

h2

A constraint matrix defining H2

m1

A vector of expected population means under H1

m2

A vector of expected populations means under H2 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

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

seed

A number. The random seed to be set

Value

The Type 1, Type 2, Decision error and Area of Indecision probability and the median BF12s under H1 and H2

Examples

# NOT RUN {
# Short example WITH SMALL AMOUNT OF SAMPLES
h1 <- matrix(c(1,-1,0,0,1,-1), nrow= 2, byrow= TRUE)
h2 <- "c"
m1 <- c(.4,.2,0)
m2 <- c(.2,0,.1)
bayes_power(40, h1, h2, m1, m2, datasets = 50, nsamp = 50)
# }