convertGeneticSigma | R Documentation |

Convert a matrix of correlations between p-values to a matrix of covariances between their chi-squared transforms.

```
convertGeneticSigma(sigma, kappa, models = chiCorMods)
```

`sigma` |
M by M correlation matrix between markers |

`kappa` |
numeric degrees of freedom |

`models` |
model object with a predict method |

This function uses models fit to large simulated data sets to convert a matrix of correlations between genetic markers the covariance matrix of chi-squared random variables gained from transforming p-values on these markers. The simulations used to create data for these models assume the p-values for each marker arise from tests of association with a common, normally distributed trait independent of all markers. As a result, this conversion function should be used only in analogous settings.

Models were fit for degrees of freedom at increments of 0.1 between -8 and 8 on the log scale, and interpolation is applied if the degrees of freedom given to the function does not fall exactly on this grid (with a warning provided to the user).

If a user wants to generalize this setting, the option to provide a custom list of models which predict based on a named argument 'zcor' is supported. Each model must have a name in the list that can be converted to a numeric, and these are assumed to be on the natural log scale.

M by M matrix of chi-squared covariances

Chirs Salahub

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