Compare the single-predictor health risks when all of the other predictors in Z are fixed to their a specific quantile to when all of the other predictors in Z are fixed to their a second specific quantile.

1 2 3 |

`fit` |
An object containing the results returned by a the |

`y` |
a vector of outcome data of length |

`Z` |
an |

`X` |
an |

`which.z` |
vector indicating which variables (columns of |

`qs.diff` |
vector indicating the two quantiles at which to compute the single-predictor risk summary |

`qs.fixed` |
vector indicating the two quantiles at which to fix all of the remaining exposures in |

`method` |
method for obtaining posterior summaries at a vector of new points. Options are "approx" and "exact"; defaults to "approx", which is faster particularly for large datasets; see details |

`sel` |
logical expression indicating samples to keep; defaults to keeping the second half of all samples |

`z.names` |
optional vector of names for the columns of |

`...` |
other argumentd to pass on to the prediction function |

If

`method == "approx"`

then calls the function`ComputePostmeanHnew.approx`

. In this case, the argument`sel`

defaults to the second half of the MCMC iterations.If

`method == "exact"`

then calls the function`ComputePostmeanHnew.exact`

. In this case, the argument`sel`

defaults to keeping every 10 iterations after dropping the first 50% of samples, or if this results in fewer than 100 iterations, than 100 iterations are kept

For guided examples and additional information, go to https://jenfb.github.io/bkmr/overview.html

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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