bayes_power {BayesianPower} | R Documentation |
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
|
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 |
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 |
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
# 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)