bacon: Gibbs sampler

In mvaniterson/bacon: Controlling bias and inflation in association studies using the empirical null distribution

Gibbs sampler

Description

Gibbs Sampler Algorithm to fit a three component normal mixture to z-scores

Usage

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))),
  globalSeed = 42,
  parallelSeed = 42
)

Arguments

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 level used to determine prop. null for starting values
na.exclude	see ?na.exclude
verbose	default FALSE
priors	list of parameters for the prior distributions
globalSeed	default 42 global seed. If set to NULL, randomization will occur for sequential and parallel bacon calls
parallelSeed	default 42 BiocParallel RNGseed. If input statistics are a matrix and globalSeed=NULL, setting parallelSeed=NULL will allow randomization across parallel processes within a bacon call and across separate calls to bacon.

Value

object of class-Bacon

Author(s)

mvaniterson

References

Implementation is based on a version from Zhihui Liu https://macsphere.mcmaster.ca/handle/11375/9368

Examples

##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))

mvaniterson/bacon documentation built on April 19, 2024, 5:32 p.m.