Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function computes the KL divergence between an observed distribution of Z-statistics and the expected distribution, when truncating at a given percentile of the reference normal distribution.

1 | ```
compute_KL(Zmat,alpha,pval)
``` |

`Zmat` |
Matrix of Z-statistics outputted from |

`alpha` |
The inner percentile of the reference normal distribution to compare to, e.g. if |

`pval` |
If marginal pre-screening was performed originally, the P-value threshold used for the marginal screening. |

This function is a vbsr internal function that computes the KL divergence for the Z-statistic distribution output by `vbsr`

if run on a grid of `l0_path`

, and takes as input the inner quantile to compute the KL statistic with (`alpha`

), and if there was already marginal pre-screening performed to remove the central part of the Z-statistic distribution (`pval`

).

`kl_vec` |
This is the observed KL statistic computed along the specified path of |

`min_kl` |
This is the minimum value of observed KL statistic |

`mean_kl` |
Random permutations are performed to determine the expected KL statistic given the number of covariates being tested, and the setting of |

`se_kl` |
The error in the KL statistics from the random permutations. Good for determining the range of KL values that is reasonable given the model fits. |

This function is an internal function, and this functionality is included primarily to include the model fit functions proposed by Logsdon et al. 2012. The regular `vbsr`

function with `post=0.95`

, produces very similar results to the KL statistic using a liberal cutoff, and `post=0.5`

produces very similar results to the more conservative cutoff proposed in Logsdon et. al. 2012, and the `post`

approaches are much more computationally efficient, since the algorithm is fit based on just a single penalty parameter.

Benjamin A. Logsdon

Logsdon, B.A., C.L. Carty, A.P. Reiner, J.Y. Dai, and C. Kooperberg (2012).
*A novel variational Bayes multiple locus Z-statistic for genome-wide association studies with Bayesian model averaging.*
*Bioinformatics, Vol. 28(13), 1738-1744*

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