Description Usage Arguments Value Author(s) References Examples

The get_cond_alpha function is to compute the conditional alpha vector for each X If only one X in the model, please use get_cond_alpha instead of get_cond_A A sub-step in the get_cond_A function

1 | ```
get_cond_alpha(alphas, Gl, N.Gx, ridgeTerm = FALSE)
``` |

`alphas` |
the marginal effects of SNPs on one exposure (Gx). |

`Gl` |
the reference panel (Gl), such as 1000 Genome |

`N.Gx` |
the sample size of the Gx. It can be a scalar. |

`ridgeTerm` |
ridgeTerm = TRUE when the matrix L is singular. Matrix L is obtained from the cholesky decomposition of G0'G0. Default as FALSE |

A vector with conditional estimates which are converted from marginal estimates using the JAM model.

Lai Jiang

Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020).
A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates
in a Mendelian Randomization or Transcriptome Analysis. *bioRxiv*
https://doi.org/10.1101/2020.02.03.924241.

1 2 3 4 | ```
data(Gl)
data(betas.Gy)
data(marginal_A)
get_cond_alpha(alphas = marginal_A[, 1], Gl = Gl, N.Gx = 339224, ridgeTerm = TRUE)
``` |

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