bayes_power {BayesianPower} | R Documentation |
Determine the 'power' for a Bayesian hypothesis test
bayes_power(n, h1, h2, m1, m2, ngroup = NULL, comp = NULL, bound1 = 1, bound2 = 1/bound1, datasets = 1000, nsamp = 1000, seed = NULL)
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 (standardized) |
m2 |
A vector of expected populations means under H2 (standardized)
|
ngroup |
A number or |
comp |
A vector or |
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 |
The Type 1, Type 2, Decision error and Area of Indecision probability and the median BF12s under H1 and H2
# 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) # Example 1 H1 vs Hc 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 = 500, nsamp = 500) # Example 2 H1 vs H2 h1 <- matrix(c(1,-1,0,0,0,1,-1,0,0,0,1,-1), nrow= 3, byrow= TRUE) h2 <- matrix(c(0,-1,1,0,0,1,0,-1,-1,0,0,1), nrow = 3, byrow= TRUE) m1 <- c(.7,.3,.1,0) m2 <- c(0,.4,.5,.1) bayes_power(40, h1, h2, m1, m2, datasets = 500, nsamp = 500)