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

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/trinomial.R

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

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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.

Author(s)

Luke W. Miratrix

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. See audit.plan.tri for theo object that 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 for an SRS audit method.

Examples

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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 May 30, 2017, 5:51 a.m.