tri.calc.sample {elec} | R Documentation |
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" )