LBL_summary: Posterior Inference for LBL

In mxw010/LBL: Logistic and Quantitative Bayesian Lasso for identifying genetic association between common diseases and rare haplotypes

Description

LBL_summary provides inferences based on the posterior samples. Specifically, this function will return posterior means, credible intervals, and Bayes Factors (BF) estimates for the haplotypic effect coefficients.

Usage

LBL_summary(output, a, b, e = 0.1, ci.level = 0.95)

Arguments

output	An object returned by LBL functions (LBL, famLBL, and cLBL) with 'summary=FALSE'.
a	First hyperparameter of the prior for regression coefficients, β. The prior variance of β is 2/λ^2 and λ has Gamma(a,b) prior. The Gamma prior parameters a and b are such that the mean and variance of the Gamma distribution are a/b and a/b^2. The value will be transferred from LBL functions.
b	Second hyperparameter of the Gamma(a,b) distribution described above. The value will be transferred from LBL functions.
e	A (small) number ε in the null hypothesis of no association, H_0: |β| ≤ ε. The default is 0.1. Changing e from the default of 0.1 may necessitate choosing a different threshold for Bayes Factor (one of the outputs) to infer association.
ci.level	Credible probability.. The probability that the true value of beta will be within the credible interval. Default is 0.95 which corresponds to a 95% posterior credible interval. Only used if 'summary = TRUE'.

Value

A list with the following components:

haplotypes

The list of haplotypes used in the analysis.

OR

Posterior mean of odds ratio.

OR.CI

95% posterior credible sets for the ORs.

BF

Bayes Factor estimates based on posterior samples. if the posterior samples are all greater than e, then BF is set to be 999.

mxw010/LBL documentation built on Sept. 26, 2021, 3:44 a.m.