bacon {bacon} | R Documentation |
Gibbs Sampler Algorithm to fit a three component normal mixture to z-scores
bacon(teststatistics = NULL, effectsizes = NULL, standarderrors = NULL, niter = 5000L, nburnin = 2000L, nbins = 1000, trim = 0.999, level = 0.05, na.exclude = FALSE, verbose = FALSE, priors = list(sigma = list(alpha = 1.28, beta = 0.36), mu = list(lambda = c(0, 3, -3), tau = c(1000, 100, 100)), epsilon = list(gamma = c(90, 5, 5))))
teststatistics |
numeric vector or matrix of test-statistics |
effectsizes |
numeric vector or matrix of effect-sizes |
standarderrors |
numeric vector or matrix of standard errors |
niter |
number of iterations |
nburnin |
length of the burnin period |
nbins |
default 1000 else bin test-statistics |
trim |
default 0.999 trimming test-statistics |
level |
significance leve used to determine prop. null for starting values |
na.exclude |
see ?na.exclude |
verbose |
default FALSE |
priors |
list of parameters of for the prior distributions |
object of class-Bacon
mvaniterson
Implementation is based on a version from Zhihui Liu https://macsphere.mcmaster.ca/handle/11375/9368
##simulate some test-statistic from a normal mixture ##and run bacon y <- rnormmix(2000, c(0.9, 0, 1, 0, 4, 1)) bc <- bacon(y) ##extract all estimated mixture parameters estimates(bc) ##extract inflation inflation(bc) ##extract bias bias(bc) ##extract bias and inflation corrected test-statistics head(tstat(bc)) ##inspect the Gibbs Sampling output traces(bc) posteriors(bc) fit(bc) ##simulate multiple sets of test-statistic from a normal mixture ##and run bacon y <- matrix(rnormmix(10*2000, c(0.9, 0, 1, 0, 4, 1)), ncol=10) bc <- bacon(y) ##extract all estimated mixture parameters estimates(bc) ##extract only the inflation inflation(bc) ##extract only the bias bias(bc) ##extract bias and inflation corrected P-values head(pval(bc)) ##extract bias and inflation corrected test-statistics head(tstat(bc))