Description Usage Arguments Value Warning Author(s) References

View source: R/bayesIndirect.R

bayesIndirect examines the indirect effect of a SNP on the outcome through the mediator in a Bayesian framework with a spike and slab prior.

1 | ```
bayesIndirect(x, k, y, z = NULL, nCov = 0, plot0 = FALSE, nIts = 50000, nBurn = 10000, propGamma1Pi = 0.7, cx = 10, pix = 0.5, SEED = 1)
``` |

`x` |
is the exposure, such as the SNP. |

`k` |
is the mediator. |

`y` |
is the normally distributed outcome. |

`z` |
is the matrix of covariates. |

`nCov` |
is the number of covariates. nCov = 0, 1 or 2. |

`plot0` |
true if density plot should be outputted or false otherwise. |

`nIts` |
is the number of iterations of the MCMC. |

`nBurn` |
is the Burn-in period. |

`propGamma1Pi` |
is the probability of gamma from the Bernoulli distribution. |

`cx` |
is the variance of beta X from the normal distribution. |

`pix` |
is the probability that beta x is sampled from the normal distribution. |

`SEED` |
is the seed for the SKAT function, default = 1. |

The posterior mean, standard deviation and quantiles for each variable.

library(coda) is needed to run this function.

Sharon Lutz, Annie Thwing

Lutz SM, Hokanson JE, Sharma S, Weiss S, Raby B, Lange C. (2013) On the Integration of Expression Profiles in Genetic Association Studies: A Bayesian Approach to Determine the Path from Gene to Disease. Open Journal of Genetics. 3(3).

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