bayes_sampsize {BayesianPower} | R Documentation |
Determine the required sample size for a Bayesian hypothesis test
bayes_sampsize(h1, h2, m1, m2, type = 1, cutoff, bound1 = 1, bound2 = 1/bound1, datasets = 1000, nsamp = 1000, minss = 2, maxss = 1000, seed = 31)
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).
|
type |
A character. The type of error to be controlled
options are: |
cutoff |
A number. The cutoff criterion for type.
If |
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 |
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
# 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, "de", .125, nsamp = 50, datasets = 50, minss = 40, maxss = 70) # Example 1 Decision error and 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_sampsize(h1, h2, m1, m2, "de", .125) # Example 2 Indecision error and 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_sampsize(h1, h2, m1, m2, type = "aoi", cutoff = .2, minss = 2, maxss = 500)