blvcm_bin: Bayesian latent variable collapsing model (BLVCM) for binary...

Description Usage Arguments Details Value Author(s) References Examples

View source: R/blvcm_bin.R

Description

The function implements BLVCM for binary traits using a Gibbs sampler with probit link function.

Usage

1
2
blvcm_bin(pheno, geno, model = 3, iter = 30000, burnin = 500, var = -1, lambda = 0.2, 
cov = 0, init = c(0, 0))

Arguments

pheno

An N x 3 phenotypic data matrix (trait, family number, zyg=1 for MZ, 2 for DZ), where N is the number of subjects. The trait must be 0 or 1.

geno

An N x K genotypic data matrix, where N is the number of subjects and K is the number of rare variants. The value can be 0 or 1. A missing genotype is represented by -9, which will be imputated by BLVCM based on HWE.

model

Twin model: 3 for ACE model, 2 for AE model, 1 for independent subjects

iter

The number of MCMC iterations (must be positive). The default value is 30000.

burnin

The number of burn-ins (must be positive). The default value is 500.

var

The variance hyperparameters (must be positive) in the priors for β and γ. The default value is 1.

lambda

The threshold λ (must be positive) for hypothesis test. The default value is 0.2.

cov

A matrix of other covariates to be adjusted.

init

Initial values for β and γ. The default values are 0. The initial value for β must be non-negative.

Details

The Gibbs sampler uses the variable augmentation method for probit link described in Albert, J. H., & Chib, S. (1993). Since the variance of a binary variable is determined by its mean compared to quantitative traits, θ(s) are eliminated to avoid overfitting.

Value

BF_main

The Bayes factor of the main effect

BF_int

The Bayes factor of the interaction effect

post_odds_beta

The posterior odds of β

post_odds_gamma

The posterior odds of γ

com_a

The inverse of the posterior mean of the precision for additive genetic component

com_c

The inverse of the posterior mean of the precision for shared environmental component

mean_mu

The posterior mean of the intercept μ

mean_beta

The posterior mean of β

mean_gamma

The posterior mean of γ

sd_mu

The posterior standard deviation of the intercept μ

sd_beta

The posterior standard deviation of β

sd_gamma

The posterior standard deviation of γ

mean_rv

The posterior mean of α

mean_cov

The posterior mean of the effects of covariates

Author(s)

Liang He

References

He, L., Sillanp<e4><e4>, M. J., Ripatti, S., & Pitk<e4>niemi, J. (2014). Bayesian Latent Variable Collapsing Model for Detecting Rare Variant Interaction Effect in Twin Study. Genetic epidemiology, 38(4), 310-324.

Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American statistical Association, 88(422), 669-679.

Examples

1
2
data(blvcm_bin_data)
blvcm_bin(blvcm_bin_data$pheno, blvcm_bin_data$geno[,1:3], iter=5000, burnin=500, model=2)

BMRV documentation built on May 29, 2017, 4:55 p.m.