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
)

Arguments

obj

output of coloc.detail or coloc.process

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

Value

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

Details

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.

Author

Chris Wallace