Description Usage Arguments Value Author(s) References Examples

The function implements BLVCM using a Gibbs sampler.

1 2 |

`pheno` |
An |

`geno` |
An |

`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 |

`lambda` |
The threshold |

`cov` |
A matrix of other covariates. |

`init` |
Initial values for |

`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 |

`post_odds_gamma ` |
The posterior odds of |

`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 |

`mean_beta ` |
The posterior mean of |

`mean_gamma ` |
The posterior mean of |

`sd_mu ` |
The posterior standard deviation of the intercept |

`sd_beta ` |
The posterior standard deviation of |

`sd_gamma ` |
The posterior standard deviation of |

`mean_rv ` |
The posterior mean of |

`mean_cov ` |
The posterior mean of the effects of covariates |

`prior_var ` |
The variance hyperparameters in the priors for |

Liang He

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.

1 2 | ```
data(blvcm_data)
blvcm(blvcm_data$pheno, blvcm_data$geno[,1:3], iter=10000, burnin=1000, model=3)
``` |

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