tri.calc.sample: Calculate needed sample size for election auditing using the...

In elec: Collection of Functions for Statistical Election Audits

Calculate needed sample size for election auditing using the Trinomial Bound

Description

Calculate an estimated sample size to do a trinomial bound that would have a specified power (the chance to certify assuming a given estimate of low-error error rate), and a specified maximum risk of erroneously certifying if the actual election outcome is wrong.

Usage

tri.calc.sample(
  Z,
  beta = 0.75,
  guess.N = 20,
  p_d = 0.1,
  swing = 5,
  power = 0.9,
  bound = c("e.plus", "WPM", "passed")
)

Arguments

Z	elec.data object
beta	1-beta is the acceptable risk of failing to notice that a full manual count is needed given an election with an actual outcome different from the semi-official outcome.
guess.N	The guessed needed sample size.
p_d	For the alternate: estimate of the proportion of precincts that have error.
swing	For the alternate: estimate of the max size of an error in votes, given that error exists.
power	The desired power of the test against the specified alternate defined by p\_d and swing.
bound	e.plus, WPM, or use the passed, previously computed, e.max values in the Z object.

Value

An audit.plan.tri object. This is an object that holds information on how many samples are needed in the audit, the maximum amount of potential overstatement in the election, and a few other things.

References

See Luke W. Miratrix and Philip B. Stark. (2009) Election Audits using a Trinomial Bound. http://www.stat.berkeley.edu/~stark

See Also

See elec.data for information on the object that holds vote counts. See tri.sample for drawing the actual sample. The audit.plan.tri object holds the audit plan information (e.g., number of draws, estimated work in ballots to audit, etc.). See trinomial.bound for analyzing the data once the audit results are in. See tri.audit.sim for simulating audits using this method. See CAST.audit for an SRS audit method.

Examples

data(santa.cruz)
Z = elec.data( santa.cruz, C.names=c("danner","leopold") )
tri.calc.sample( Z, beta=0.75, guess.N = 10, p_d = 0.05,
               swing=10, power=0.9, bound="e.plus" )

elec documentation built on April 27, 2022, 1:05 a.m.