Shows how prior and posterior per-hypothesis probabilities change as a function of p12

sensitivity( obj, rule = "", dataset1 = NULL, dataset2 = NULL, npoints = 100, doplot = TRUE, plot.manhattans = TRUE, preserve.par = FALSE, row = 1 )

obj | output of coloc.detail or coloc.process |
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rule | a decision rule. This states what values of posterior probabilities "pass" some threshold. This is a string which will be parsed and evaluated, better explained by examples. "H4 > 0.5" says post prob of H4 > 0.5 is a pass. "H4 > 0.9 & H4/H3 > 3" says post prob of H4 must be > 0.9 AND it must be at least 3 times the post prob of H3." |

dataset1 | optional the dataset1 used to run SuSiE. This will be used to make a Manhattan plot if plot.manhattans=TRUE. |

dataset2 | optional the dataset2 used to run SuSiE. This will be used to make a Manhattan plot if plot.manhattans=TRUE. |

npoints | the number of points over which to evaluate the prior values for p12, equally spaced on a log scale between p1*p2 and min(p1,p2) - these are logical limits on p12, but not scientifically sensible values. |

doplot | draw the plot. set to FALSE if you want to just evaluate the prior and posterior matrices and work with them yourself |

plot.manhattans | if TRUE, show Manhattans of input data |

preserve.par | if TRUE, do not change par() of current graphics device - this is to allow sensitivity plots to be incoporated into a larger set of plots, or to be plot one per page on a pdf, forexample |

row | when coloc.signals() has been used and multiple rows are returned in the coloc summary, which row to plot |

list of 3: prior matrix, posterior matrix, and a pass/fail indicator (returned invisibly)

Function is called mainly for plotting side effect. It draws two plots, showing how prior and posterior probabilities of each coloc hypothesis change with changing p12. A decision rule sets the values of the posterior probabilities considered acceptable, and is used to shade in green the region of the plot for which the p12 prior would give and acceptable result. The user is encouraged to consider carefully whether some prior values shown within the green shaded region are sensible before accepting the hypothesis. If no shading is shown, then no priors give rise to an accepted result.

Chris Wallace