spikes: Fraud-detection from vote-share data

In spikes: Detecting Election Fraud from Irregularities in Vote-Share Distributions

Description

Implements the resampled kernel density method to detect the excess number of election results with coarse vote-shares (a coarse vote-share is a fraction with a low denominator).

Usage

spikes(data, resamples = 1000, bw = 1e-04, grid = 1001, out = NULL)

Arguments

data	Data frame with three columns with names N (number of votes), t (number who turned out to vote), and v (number who voted for the party the votes of which are being analyzed). Returns error if columns are named incorrectly.
resamples	Number of resamples; default 1000
bw	Bandwidth for kernel density; default 0.0001
grid	Number of points on which the density is estimated; default 1001
out	Object containing parameters of beta-mixture model. If spikes has been called earlier, then out = output$out will skip density estimation and proceed directly to resampling.

Value

spikes returns object of class out.

fraud	Estimated percentage of polling stations with fraud.
ymax	Upper envelope of kernel density samples.
w	Weights for each bin: the proportion of observations falling into a bin.
out	Maximum likelihood estimates of the mixture beta binomial parameters for turnout and votes.
data	Data used in estimation.

See Also

See Also plot.out, summary.out

Examples

data(data)
## Not run: 
out <- spikes(data, resamples = 1000)

## End(Not run)

spikes documentation built on May 2, 2019, 7:55 a.m.