blvcm: Bayesian latent variable collapsing model (BLVCM)

Description Usage Arguments Value Author(s) References Examples

View source: R/blvcm.R

Description

The function implements BLVCM using a Gibbs sampler.

Usage

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blvcm(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. Please see the example data for more details. For faster convergence, it is recommanded that the phenotype should be standardized.

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, which must be positive.

burnin

The number of burn-ins, which must be positive.

var

The variance hyperparameter (must be positive) in the priors for β and γ. If not specified (var=-1), the default value is the variance of the phenotype.

lambda

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

cov

A matrix of other covariates.

init

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

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. NA for independent samples

com_c

The inverse of the posterior mean of the precision for shared environmental component. NA for independent samples or AE model

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. The number of α equals the number of RVs

mean_cov

The posterior mean of the effects of covariates

prior_var

The variance hyperparameters in the priors for β and γ

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.

Examples

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data(blvcm_data)
blvcm(blvcm_data$pheno, blvcm_data$geno[,1:3], iter=10000, burnin=1000, model=3)

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