qb.BayesFactor {qtlbim}R Documentation

Bayesian model selection via Bayes factors.

Description

Model-averaged posteriors and Bayes factors computed for number and pattern of QTL, chromosomes and pairs of chromosomes showing epistasis.

Usage

qb.bf(...)
qb.BayesFactor(qbObject, items = c("nqtl","pattern","chrom","pairs"),
  cutoff.pattern, cutoff.pairs = 1, nmax = 15, epistasis = TRUE, ...)
## S3 method for class 'qb.BayesFactor':
plot(x, ...)
## S3 method for class 'qb.BayesFactor':
summary(object, sort = TRUE, digits = 3, ...)
## S3 method for class 'qb.BayesFactor':
print(x, ...)

Arguments

qbObject An object of class qb.
object Object of class qb.BayesFactor.
x Object of class qb.BayesFactor.
items Items to include in model selection assessment. These are nqtl = Number of QTLs; pattern = Pattern of QTL across chromosomes as comma-separated chromosome numbers and colon-separated chromosome pairs; chrom = Chromosome; pairs = Epistatic pairs of chromosomes.
cutoff.pattern Percent cutoff for pattern inclusion in model selection. Default is 0.25 (0.5) if epistasis is TRUE (FALSE).
cutoff.pairs Percent cutoff for epistatic pair inclusion in model selection.
nmax Maximum number of model terms included per item (for items "pattern" and "pairs" only).
epistasis Include epistasis in patterns if TRUE.
sort Sort by Bayes factor if TRUE.
digits Number of significant digits for summary.
... Additional arguments passed to generic plot, summary or print.

Details

qb.BayesFactor (or qb.bf for short) creates model selection results for selected items. These are based on marginal posteriors and priors, averaged over all other model parameters. The posterior may be influenced by prior, while Bayes factors are empirically less sensitive for QTL model selection. The Bayes factors are computed relative to the smallest term for each item, using the ratios of posterior/prior. Any pair of model terms can be compared as the ratio of their Bayes factors.

Value

List with items, each containing:

posterior Posterior frequency of MCMC samples.
prior Prior frequency.
bf Rank-ordered Bayes factors relative to smallest value.
bfse Approximate standard error for bf computed using binomial variance of MCMC samples.

Author(s)

Brian S. Yandell, yandell@stat.wisc.edu

References

http://www.qtlbim.org

See Also

plot.qb, qb.mcmc

Examples

data(qbExample)

temp <- qb.BayesFactor(qbExample)
summary(temp)
plot(temp)

[Package qtlbim version 1.9.3 Index]