tri.audit.sim {elec}

tri.audit.sim

Description

This is a SIMULATION FUNCTION, and is not used for actual auditing of elections.

Usage

tri.audit.sim(
  Z,
  n,
  p_d = 0.1,
  swing = 5,
  return.type = c("statistics", "taints", "precinct"),
  seed = NULL,
  PID = "PID",
  ...
)

Arguments

Z

elec.data object.

n

Sample size to draw.

p_d

The probability of a precinct having an error.

swing

The size of the error, in votes.

return.type

What kind of results to return: "statistics","taints", or "precinct"

seed

Random seed to use.

PID

Column name of column holding unique precinct IDs

...

Extra arguments passed to tri.sample

Details

Given a matrix of votes, calculate the weights for all precincts and then draw a sample (using tri.sample). Then, assuming that p\_d percent of the precincts (at random) have error, and the errors are due to vote miscounts of size 'swing', conduct a simulated “audit”, returning the found descrepancies.

Value

List of taints found in such a circumstance OR precincts selected with relevant attributes (including simulated errors, if asked) OR the number of non-zero taints and the size of largest taint.

Author(s)

Luke W. Miratrix

See Also

elec.data for the object that holds vote data. See tri.calc.sample for computing sample sizes for trinomial bound audits.

Examples

  data(santa.cruz)
  Z = elec.data(santa.cruz, C.names=c("leopold","danner"))
  Z$V$e.max = maximumMarginBound( Z )
  ## Sample from fake truth, see how many errors we get.
  tri.audit.sim( Z, 10,  p_d=0.25, swing=10, return.type="precinct" )

  ## what does distribution look like?
  res = replicate( 200, tri.audit.sim( Z, 10,  p_d=0.25, swing=10 ) )
  apply(res,1, summary) 
  hist( res[2,], main="Distribution of maximum size taint" )