Package 'SafeVote'

Description

.print method for summary object

Usage

.print.summary.SafeVote(x, ...)

Arguments

Value

undocumented

Description

summarises vote-totals for subsequent printing

Usage

.summary.SafeVote(object, larger.wins = TRUE, reorder = TRUE)

Arguments

Value

a data.frame with three columns and nc+1 rows, where nc is the number of candidates. The first column contains candidate names and a final entry named "Sum". The second column contains vote totals. The third column is a vector of chars which indicate whether the candidate has been elected. The data.frame has four named attributes carrying election parameters.

TODO: refactor into a modern dialect of R, perhaps by defining a constructor for an election_info S3 object with a summary method and a print method

Description

This data is one of 87 sets of ballots from the Tideman data collection, as curated by The Center for Range Voting.

This set of ballots was collected in 1987 by Nicolaus Tideman, with support from NSF grant SES86-18328. "The data are records of ballots from elections of British organizations (mostly trade unions using PR-STV or IRV voting) in which the voters ranked the candidates. The data were gathered under a stipulation that the organizations involved would remain anonymous."

The ballots were encoded in David Hill's format, and have been converted to the preference-vector format of this package. The archival file A4.HIL at rangevoting.org contains eight blank ballot papers (1, 616, 619, 620, 685, 686, 687, 688) which we have retained. This set may be counted by 'stv(a3_hil,nseats=attr(a3_hil,"nseats"))'.

Usage

data(a3_hil)

Format

A data frame with attribute "nseats" = 7, consisting of 989 observations and 15 candidates.

Description

This data is one of 87 sets of ballots from the Tideman data collection, as curated by The Center for Range Voting. The ballots were archived in David Hill's format, and have been converted to the preference-vector format of this package.

This set of ballots was collected in 1987 by Nicolaus Tideman, with support from NSF grant SES86-18328. "The data are records of ballots from elections of British organizations (mostly trade unions using PR-STV or IRV voting) in which the voters ranked the candidates. The data were gathered under a stipulation that the organizations involved would remain anonymous."

Usage

data(a4_hil)

Format

A data frame with attribute "nseats" = 2, consisting of 43 observations and 14 candidates.

Description

This data is one of 87 sets of ballots from the Tideman data collection, as curated by The Center for Range Voting.

This set of ballots was collected in 1988 by Nicolaus Tideman, with support from NSF grant SES86-18328. "The data are records of ballots from elections of British organizations (mostly trade unions using PR-STV or IRV voting) in which the voters ranked the candidates. The data were gathered under a stipulation that the organizations involved would remain anonymous."

The ballots were encoded in David Hill's format, and have been converted to the preference-vector format of this package. Candidates have been renamed to letters of the alphabet, for ease of comparison with Table 3 of Tideman (2000). Note: the DOI for this article is 10.1023/A:1005082925477, with an embedded colon which isn't handled by the usual DOI-to-URL conversions.

As noted in this table, it is a very close race between candidates D, F, and B in the final rounds of a Meek count of 'a53_hil'.

Tideman's implementation of Meek's method excludes B (on 59.02 votes), then elects D in the final round (on 88.33 votes) with a margin of 0.95 votes ahead of F (on 87.38 votes).

In v1.0, 'stv(a53.hil,quota.hare=TRUE)' excludes F (on 56.418 votes), then elects D in the final round (on 79.705 votes) with a winning margin of 0.747 votes ahead of B (on 78.958 votes). The result of the election is the same but the vote counts and winning margins differ significantly; so we conclude that 'stv(quota.hare=TRUE)' in SafeVote v1.0 is not a reliable proxy for Tideman's implementation of Meek's algorithm.

Future researchers may wish to adjust the quota calculation of 'vote.stv()' so that it is no longer biased upward by a "fuzz" of 0.001, to see if this change significantly reduces the discrepancies with Tideman's implementation of Meek.

It would be unreasonable to expect an exact replication of results from two different implementations of an STV method. We leave it to future researchers to develop a formal specification, so that it would be possible to verify the correctness of an implementation. We also leave it to future researchers to develop a set of test cases with appropriate levels of tolerance for the vagaries of floating-point roundoff in optimised (or even unoptimised!) compilations of the same code on different computing systems. We suggest that 'a53_hil' be included in any such test set.

We note in passing that B.A. Wichmann, in "Checking two STV programs", Voting Matters 11, 2000, discussed the cross-validation exercise he conducted between the ERBS implementation of its voting rules and the Church of England's implementation of its voting rules. In both cases, he discovered ambiguities in the specification as well as defects in the implementation.

Usage

data(a53_hil)

Format

A data frame with attribute "nseats" = 4, consisting of 460 observations and 10 candidates.

Description

See https://arxiv.org/abs/2102.05801

Usage

approval(votes, nseats = 1, fsep = "\t", quiet = FALSE, ...)

Arguments

Value

undocumented

Description

as.SafeRankExpt()

Usage

as.SafeRankExpt(df)

Arguments

Value

a SafeRankExpt object, or stop() if df fails some sanity checks

Description

undocumented internal method

Usage

assemble.args.for.check.score(x, max.score = NULL, ...)

Arguments

Value

undocumented

Description

undocumented internal method

Usage

assemble.args.for.check.stv(x, equal.ranking = FALSE, ...)

Arguments

Value

undocumented

Description

Undocumented internal method

Usage

backwards.tiebreak(prefs, icans, elim = TRUE)

Arguments

Description

parameter-checking method for nseats (internal)

Usage

check.nseats(
  nseats = NULL,
  ncandidates,
  default = 1,
  mcan = NULL,
  complete.ranking = FALSE
)

Arguments

Value

a valid non-NULL value for the number of seats to be filled

Description

check the validity of a partial ranking

Usage

check.ranking(r)

Arguments

Value

a partial ranking of the elements of 'r', using 'ties.method="min"'

Description

undocumented internal method

Usage

check.votes(x, ..., quiet = FALSE)

Arguments

Value

undocumented

Description

undocumented internal method

Usage

check.votes.approval(record, ...)

Arguments

Value

undocumented

Description

undocumented internal method

Usage

check.votes.condorcet(record, ...)

Arguments

Value

undocumented

Description

undocumented internal method

Usage

check.votes.plurality(record, ...)

Arguments

Value

undocumented

Description

undocumented internal method

Usage

check.votes.score(record, max.score, ...)

Arguments

Value

undocumented

Description

undocumented internal method

Usage

check.votes.stv(record, equal.ranking = FALSE, ...)

Arguments

Value

undocumented

Description

undocumented internal method

Usage

check.votes.tworound.runoff(record, ...)

Arguments

Value

undocumented

Description

the least upper bound on a pair of rankings

Usage

combineRankings(r1, r2)

Arguments

Value

the most complete (but possibly partial) ranking which is consistent with both r1 and r2. Uses 'ties.method="min"'

Examples

combineRankings( c(3,1,2), c(2,1,3) )

Description

internal method to analyse the partial results of an stv() ballot count, to discover a complete ranking of all candidates. The ranking may depend on the value of nseats, because this affects how votes are transferred.

Usage

completeRankingTable(object, quiet, verbose)

Arguments

Value

data.frame with columns TotalRank, Margin, Candidate, Elected, SafeRank

Description

The Condorcet method elects the candidate who wins a majority of the ranked vote in every head to head election against each of the other candidates. A Condorcet winner is a candidate who beats all other candidates in pairwise comparisons. Analogously, a Condorcet loser is a candidate who loses against all other candidates. Neither Condorcet winner nor loser might exist.

Usage

condorcet(
  votes,
  runoff = FALSE,
  nseats = 1,
  safety = 1,
  fsep = "\t",
  quiet = FALSE,
  ...
)

Arguments

Details

If the runoff argument is set to 'TRUE' and no Condorcet winner exists, two or more candidates with the most pairwise wins are selected and the method is applied to such subset. If more than two candidates are in such run-off, the selection is performed repeatedly, until either a winner is selected or no more selection is possible.

The input data votes is structured the same way as for the stv method: Row 'i' contains the preferences of voter 'i' numbered '1; 2; : : : ; r; 0; 0; 0; 0', in some order, while equal preferences are allowed. The columns correspond to the candidates. The dimnames of the columns are the names of the candidates; if these are not supplied then the candidates are lettered 'A, B, C, ...'. If the dataset contains missing values (NA), they are replaced by zeros.

If a ballot has equally-ranked candidates, its rankings are tested for validity: for each preference $i$ which does not have any duplicate, there are exactly $i - 1$ preferences $j$ with $0 < j < i$. If any ballot 'x' fails this validity test, it is automatically corrected (aka "converted") into a valid ballot using 'x <- rank(x, ties.method = "min")', and a warning is issued.

This method also computes a Borda ranking of all candidates, using tournament-style scoring. This ranking is "fuzzed" into a 'safeRank', with approximately 1 s.d. of fuzz when 'safety=1.0' and voter preferences are i.u.d. A warning is thrown if a 'safeRank' violates the (extended) Condorcet principle: that Candidate $i$ is more highly ranked than Candidate $j$ only if a majority of voters agree with this.

Value

Object of class 'SafeVote.condorcet'

Examples

{
data(food_election)
condorcet(food_election)
}

Description

The 'correct.ranking' function returns a modified set of ballots. Its argument 'partial' determines if ballots are partially set to '0' ('TRUE'), or if it is a complete re-ranking, as allowed when 'equal.ranking = TRUE'. It can be used by calling it explicitly. It is called by 'stv' if 'equal.ranking = TRUE' or 'invalid.partial = TRUE'. It is also called from within the 'condorcet' function with the default value ('FALSE') for 'partial', i.e. interpreting any '0' as a last= preference.

Usage

correct.ranking(votes, partial = FALSE, quiet = FALSE)

Arguments

Value

corrected ballots

Description

Dataset containing ranked votes for the Dublin West constituency in 2002, Ireland.

Usage

data(dublin_west)

Format

A data frame with 29988 observations and 9 candidates. Each record corresponds to one ballot with candidates being ranked between 1 and 9 with zeros allowed.

See Also

Wikipedia

Description

prints the basic results of an election

Usage

election.info(x)

Arguments

Value

data.frame : an invisible copy of the printed results

TODO: refactor into a modern dialect of R, e.g. defining a constructor for an election_info S3 object with a print method

Description

extract margins from the results of a ballot count

Usage

extractMargins(marginNames, crRanks, cr)

Arguments

Value

named list of margins

Description

Extract a ranking vector by name from the results of a ballot count

Usage

extractRank(rankMethod, cr)

Arguments

Value

a numeric ranking vector, in order of colnames(cr$data)

Description

Sample data for testing SafeVote

Usage

data(food_election)

Format

A data frame with 20 observations and 5 candidates (Oranges, Pears, Chocolate, Strawberries, Sweets). Each record corresponds to one ballot with ranking for each of the candidates.

Description

Undocumented internal method

Usage

forwards.tiebreak(prefs, icans, elim = TRUE)

Arguments

Description

The image function visualizes the joint distribution of two preferences (if 'all.pref=FALSE') given 'xpref' and 'ypref', as well as the marginal distribution of all preferences (if 'all.pref=TRUE'). The joint distribution can be shown as proportions (if 'proportion=TRUE') or raw vote counts (if 'proportion=FALSE').

Usage

## S3 method for class 'SafeVote.condorcet'
image(x, ...)

Arguments

Value

image object, with side-effect in RStudio Plots pane

Description

visualisation of joint and marginal distributions in STV preferences

Usage

## S3 method for class 'SafeVote.stv'
image(x, xpref = 2, ypref = 1, all.pref = FALSE, proportion = TRUE, ...)

Arguments

Value

image object, with side-effect in RStudio Plots pane

Description

Modified version of ims_election, for use in approval voting.

Usage

data(ims_approval)

Format

A data frame with 620 observations and 10 candidates (names were made up). Each record corresponds to one ballot, with 0 indicating disapproval of a candidate and 1 indicating approval.

Description

Datasets containing anonymized votes for a past Council election of the Institute of Mathematical Statistics (IMS). The dataset ims_election is the original dataset used with single transferable vote, where candidate names have been changed.

Usage

data(ims_election)

Format

A data frame with 620 observations and 10 candidates (names were made up). Each record corresponds to one ballot. The IMS Council voting is done using the STV method, and thus the ims_election dataset contains ballots with candidates being ranked between 1 and 10 with zeros allowed.

Description

Modified version of ims_election, for use in plurality voting.

Usage

data(ims_plurality)

Format

A data frame with 620 observations and 10 candidates (names were made up). Each record corresponds to one ballot, with 1 against the voter's most-preferred candidate and 0 against all other candidates.

Description

Modified version of ims_election, for use in score voting.

Usage

data(ims_score)

Format

A data frame with 620 observations and 10 candidates (names were made up). Each record corresponds to one ballot, with higher values indicating the more-preferred candidates.

Description

Copy of ims_election, included for backwards compatibility.

Usage

data(ims_election)

Format

A data frame with 620 observations and 10 candidates (names were made up). Each record corresponds to one ballot. The IMS Council voting is done using the STV method, and thus the ims_election dataset contains ballots with candidates being ranked between 1 and 10 with zeros allowed.

Description

This method was added Jan 2022 – it was named in a warning message but had apparently either never been implemented, or had been "lost" through versioning.

Usage

invalid.votes(x)

Arguments

Value

matrix with one column per candidate and one row per invalid ballot

Description

is.SafeRankExpt()

Usage

is.SafeRankExpt(x)

Arguments

Value

TRUE if x is a valid SafeRankExpt object

Description

undocumented internal method

Usage

is.valid.vote(x, method, ...)

Arguments

Value

undocumented

Description

Find a loser and their margin of victory

Usage

loserMargin(votes)

Arguments

Value

length-2 vector: the index of a losing candidate, and their margin of loss (0 if a tie, NA if no winners)

Description

Constructor for the results of a SafeRank experiment

Usage

new_SafeRankExpt(
  rankNames = list(),
  marginNames = list(),
  countMethod = character(0),
  rankMethod = character(0),
  datasetName = character(0),
  experimentalMethod = character(0),
  countArgs = list(),
  nseats = integer(0),
  otherFactors = list(),
  unitFactors = list()
)

Arguments

Value

object of class SafeRankExpt

Description

Undocumented internal method

Usage

ordered.preferences(vmat)

Arguments

Description

Undocumented internal method

Usage

ordered.tiebreak(vmat, seed = NULL)

Arguments

Description

The "adjusted rank" of a candidate is their ranking $r$ plus their scaled "winning margin". The scaled margin is $e^{-cx/\sqrt{n}}$, where $x$ is the adjusted margin (i.e. the number of votes by which this candidate is ahead of the next-weaker candidate, adjusted for the number of ballots $n$ and the number of seats $s$), and $c>0$ is the margin-scaling parameter 'cMargin'.

Usage

## S3 method for class 'SafeRankExpt'
plot(
  x,
  facetWrap = FALSE,
  nResults = NA,
  anBallots = 0,
  cMargin = 1,
  xlab = "Ballots",
  ylab = "Adjusted Rank",
  title = NULL,
  subtitle = "(default)",
  line = TRUE,
  boxPlot = FALSE,
  boxPlotCutInterval = 10,
  pointSize = 1,
  ...
)

Arguments

Details

The default value of 'cMargin=1.0' draws visual attention to candidates with a very small winning margin, as their adjusted rank is very near to $r+1$. Candidates with anything more than a small winning margin have only a small rank adjustment, due to the exponential scaling.

A scaling linear in $s/n$ is applied to margins when 'anBallots>0'. Such a linear scaling may be a helpful way to visualise the winning margins in STV elections because the margin of victory for an elected candidate is typically not much larger than the quota of $n/(s+1)$ (Droop) or $n/s$ (Hare). The linear scaling factor is $as/n$, where $a$ is the value of 'anBallots', $s$ is the number of seats, and $n$ is the number of ballots. For plotting on the (inverted) adjusted rank scale, the linearly-scaled margin is added to the candidate's rank. Note that the linearly-scaled margins are zero when $a=0$, and thus have no effect on the adjusted rank. You might want to increase the value of 'anBallots', starting from 1.0, until the winning candidate's adjusted rank is 1.0 when all ballots are counted, then confirm that the adjusted ranks of other candidates are still congruent with their ranking (i.e. that the rank-adjustment is less than 1 in all cases except perhaps on an initial transient with small numbers of ballots).

When both 'anBallots' and 'cMargins' are non-zero, the ranks are adjusted with both exponentially-scaled margins and linearly-scaled margins. The resulting plot would be difficult to interpret in a valid way.

Todo: Accept a list of SafeVoteExpt objects.

Todo: Multiple counts with the same number of ballots could be summarised with a box-and-whisker graphic, rather than a set of jittered points.

Todo: Consider developing a linear scaling that is appropriate for plotting stochastic experimental data derived from Condorcet elections.

Value

graphics object, with side-effect in RStudio Plots pane

Description

The 'plot' function shows the evolution of the total score for each candidate as well as the quota.

Usage

## S3 method for class 'SafeVote.stv'
plot(x, xlab = "Count", ylab = "Preferences", point.size = 2, ...)

Arguments

Value

graphics object, with side-effect in RStudio's Plots pane

Description

See https://arxiv.org/abs/2102.05801

Usage

plurality(votes, nseats = 1, fsep = "\t", quiet = FALSE, ...)

Arguments

Value

undocumented

Description

Coerce input 'data' into a matrix

Usage

prepare.votes(data, fsep = "\n")

Arguments

Value

a matrix with one row per ballot, one column per candidate, with named rows and columns

Description

Print method for summary.SafeRankExpt

Usage

## S3 method for class 'summary.SafeRankExpt'
print(x, ...)

Arguments

Value

invisible(x), with side-effects to console

Description

print method for summary object

Usage

## S3 method for class 'summary.SafeVote.approval'
print(x, ...)

Arguments

Value

undocumented

Description

print method for summary.SafeVote.condorcet

Usage

## S3 method for class 'summary.SafeVote.condorcet'
print(x, ...)

Arguments

Value

textual description of 'x'

Description

print method for summary of plurality object

Usage

## S3 method for class 'summary.SafeVote.plurality'
print(x, ...)

Arguments

Value

undocumented

Description

print method for summary.score object

Usage

## S3 method for class 'summary.SafeVote.score'
print(x, ...)

Arguments

Value

undocumented

Description

print() method for a summary() of a SafeVote result

Usage

## S3 method for class 'summary.SafeVote.stv'
print(x, ...)

Arguments

Value

no return value, called for side-effect of printing to console

Description

add a row to a SafeRankExpt object

Usage

rbind_SafeRankExpt(object, row)

Arguments

Value

SafeRankExpt object with an additional row

Description

rangevoting.org/TidemanData.html: The data are in a format developed by David Hill. The first line contains the number of candidates and the number to be elected. (Many but not all elections were multi-winner.) In subsequent lines that represent ballot papers, the first number is always 1. (The format was designed for a counting program that treats the first number as the number of instances of the ordering of the candidates on the line.) Next on these lines is a sequence of numbers representing a voter's reported ranking: The number of the candidate ranked first, the number of the candidate ranked second, and so on. The end of the reported ranking is signaled by a zero. A zero at the beginning of the ranking is a signal that the list of ballot papers has ended. Next come the names of the candidates, each in parentheses, as required by the counting program, and finally the name of the election.

Usage

readHil(filnm, quiet = FALSE)

Arguments

Value

a matrix with one row per ballot, one column per candidate, with named rows and columns, and with attributes "nseats" and "ename"

Description

Remove a candidate, amending ballot papers as required

Usage

remove.candidate(votes, can, quiet = TRUE)

Arguments

Value

amended ballot box

Description

See https://arxiv.org/abs/2102.05801

Usage

score(
  votes,
  nseats = 1,
  max.score = NULL,
  larger.wins = TRUE,
  fsep = "\t",
  quiet = FALSE,
  ...
)

Arguments

Value

undocumented

Description

Undocumented internal method, renamed from 'solve.tiebreak' to avoid confusion with generic solve()

Usage

solveTiebreak(method, prefs, icans, ordered.ranking = NULL, elim = TRUE)

Arguments

Value

undocumented

Description

The 'votes' parameter is as described in condorcet() with the following additional semantics.

Usage

stv(
  votes,
  nseats = NULL,
  eps = 0.001,
  equal.ranking = FALSE,
  fsep = "\t",
  ties = c("f", "b"),
  quota.hare = FALSE,
  constant.quota = FALSE,
  win.by.elim = TRUE,
  group.nseats = NULL,
  group.members = NULL,
  complete.ranking = FALSE,
  invalid.partial = FALSE,
  verbose = FALSE,
  seed = NULL,
  quiet = FALSE,
  digits = 3,
  backwards.compatible = FALSE,
  safety = 1,
  ...
)

Arguments

Details

By default the preferences are not allowed to contain duplicates per ballot. However, if the argument 'equal.ranking' is set to 'TRUE', ballots are allowed to have the same ranking for multiple candidates. The desired format is such that for each preference $i$ that does not have any duplicate, there must be exactly $i – 1$ preferences $j$ with $0 < j < i$. For example, valid ordered preferences are '1; 1; 3; 4; …', or '1; 2; 3; 3; 3; 6; …', but NOT '1; 1; 2; 3; …', or NOT '1; 2; 3; 3; 3; 5; 6; …'. If the data contain such invalid votes, they are automatically corrected and a warning is issued by calling the 'correct.ranking' function.

If equal ranking is not allowed ('equal.ranking = FALSE'), the argument 'invalid.partial' can be used to make ballots containing duplicates or gaps partially valid. If it is 'TRUE', a ballot is considered valid up to a preference that is in normal case not allowed. For example, ballots '1; 2; 3; 4; 4; 6' or '1; 2; 3; 5; 6; 7' would be both converted into '1; 2; 3; 0; 0; 0', because the ballots contain valid ranking only up to the third preference.

By default, ties in the STV algorithm are resolved using the forwards tie-breaking method, see Newland and Briton (Section 5.2.5). Argument 'ties' can be set to ”b” in order to use the backwards tie-breaking method, see O’Neill (2004). In addition, both methods are complemented by the following “ordered” method: Prior to the STV election candidates are ordered by the number of first preferences. Equal ranks are resolved by moving to the number of second preferences, then third and so on. Remaining ties are broken by random draws. Such complete ordering is used to break any tie that cannot be resolved by the forwards or backwards method. If there is at least one tie during the processing, the output contains a row indicating in which count a tie-break happened (see the 'ties' element in the Value section for an explanation of the symbols).

The ordered tiebreaking described above can be analysed from outside of the 'stv' function by using the 'ordered.tiebreak' function for viewing the a-priori ordering (the highest number is the best and lowest is the worst). Such ranking is produced by comparing candidates along the columns of the matrix returned by 'ordered.preferences'.

Value

object of class 'vote.stv'. Note: the winning margins in this object are valid for the elected candidates and their (total) ranking, but must be adjusted within tiegroups to be valid for the candidates' (possibly partial) safeRank.

Examples

data(food_election)
stv(food_election, safety = 0.0)
stv(food_election, nseats = 2)

Description

summary method for SafeRankExpt

Usage

## S3 method for class 'SafeRankExpt'
summary(object, ...)

Arguments

Value

summary.SafeRankExpt object

Description

summary method for approval results

Usage

## S3 method for class 'SafeVote.approval'
summary(object, ...)

Arguments

Value

undocumented

Description

Summary method for condorcet() results

Usage

## S3 method for class 'SafeVote.condorcet'
summary(object, ...)

Arguments

Value

data.frame object

Description

summary method for plurality object

Usage

## S3 method for class 'SafeVote.plurality'
summary(object, ...)

Arguments

Value

descriptive dataframe

Description

summary method for score object

Usage

## S3 method for class 'SafeVote.score'
summary(object, ...)

Arguments

Value

undocumented

Description

summary() method for a SafeVote result

Usage

## S3 method for class 'SafeVote.stv'
summary(object, ..., digits = 3)

Arguments

Value

data.frame summarising 'object', for use by 'print' method

Description

Renamed from 'sum.votes' to avoid confusion with the generic sum()

Usage

sumOfVotes(votes)

Arguments

Value

vector of votes for each candidate

Description

Ballots are added until a specified number of simulated elections ('arep') have been held. If a 'favoured' candidate is specified, then the ballot-box is stuffed with ballots awarding first-preference to this candidate. Alternatively, a 'tacticalBallot' may be specified. If both 'favoured' and 'tacticalBallot' are 'NULL', then a random candidate is selected as the favoured one.

Usage

testAdditions(
  votes,
  ainc = 1,
  arep = NULL,
  favoured = NULL,
  tacticalBallot = NULL,
  rankMethod = "safeRank",
  countMethod = "stv",
  countArgs = list(),
  exptName = NULL,
  equiet = FALSE,
  everbose = FALSE
)

Arguments

Value

A matrix of experimental results, of dimension $n$ by $2m+1$, where $n$ is the number of elections and $m$ is the number of candidates. The first column is named "nBallots". Other columns indicate the ranking of the eponymous candidate, and their margin over the next-lower-ranked candidate.

Examples

data(food_election) 
testAdditions(food_election, arep = 2, favoured = "Strawberries", 
  countArgs = list(safety = 0))

Description

Ballots are deleted at random from the ballot-box, with election results computed once per 'dinc' ballot-deletions. The experiment terminates after a specified number of ballots have been deleted, or a specified number of ballot-counts have occurred. Note: these ballot-counts are correlated. Use testFraction() to experiment with independently-drawn samples from the ballot-box.

Usage

testDeletions(
  votes,
  countMethod = "stv",
  countArgs = list(),
  dstart = NULL,
  dinc = NULL,
  dlimit = NULL,
  drep = NULL,
  rankMethod = "safeRank",
  exptName = NULL,
  equiet = FALSE,
  everbose = FALSE
)

Arguments

Value

'SafeRankExpt' object, describing this experiment and its results

Examples

data(food_election)
testDeletions(food_election)
testDeletions(food_election, countMethod="stv",
  countArgs=list(complete.ranking=TRUE))

Description

Starting from some number ('astart') of randomly-selected ballots, an increasingly-large collection of randomly-selected ballots are counted. The ballots are chosen independently without replacement for each experimental unit; if you want to count decreasingly-sized portions of a single sample of ballots, use testDeletions().

Usage

testFraction(
  votes = NULL,
  astart = NULL,
  ainc = NULL,
  arep = NULL,
  trep = NULL,
  rankMethod = "safeRank",
  countMethod = "stv",
  countArgs = list(),
  exptName = NULL,
  equiet = FALSE,
  everbose = FALSE
)

Arguments

Value

SafeRankExpt object of experimental results.

Examples

data(food_election)
testFraction(food_election, countMethod="condorcet",
             countArgs=list(safety=0.5,complete.ranking=TRUE))
testFraction(dublin_west, astart=20, ainc=10, arep=2, trep=3, 
             countMethod="stv", rankMethod="elected", equiet=FALSE)

Description

Undocumented internal method from original code

Usage

translate.ties(ties, method)

Arguments

Value

undocumented

Description

These are the ballots cast by Labour MPs and MEPs in an election of their party's leader in 2010, as published by the Manchester Guardian. The names of the electors have been suppressed in this file, but are available at rangevoting.org, along with extensive commentary on the election.

Usage

data(uk_labour_2010)

Format

A data frame with 266 observations and 5 candidates.

Description

generic view() for classes defined in this package

Usage

view(object, ...)

Arguments

Value

html-formatted object, with side-effect in RStudio's Viewer pane

Description

view method for approval object

Usage

## S3 method for class 'SafeVote.approval'
view(object, ...)

Arguments

Value

undocumented

Description

view method for SafeVote.condorcet

Usage

## S3 method for class 'SafeVote.condorcet'
view(object, ...)

Arguments

Value

view object

Description

view method for plurality object

Usage

## S3 method for class 'SafeVote.plurality'
view(object, ...)

Arguments

Value

undocumented

Description

view method for score object

Usage

## S3 method for class 'SafeVote.score'
view(object, ...)

Arguments

Value

undocumented

Description

view method for the result of an stv() ballot-count

Usage

## S3 method for class 'SafeVote.stv'
view(object, ...)

Arguments

Value

html-formatted object

Description

Find a winner and their margin of victory

Usage

winnerMargin(votes)

Arguments

Value

length-2 vector: the index of a winning candidate, and their margin of victory (0 if a tie, NA if no losers)

Description

This data follows the structure of a 2016 Yale Faculty Senate election, with candidate names anonymised and permuted. Imported to SafeVote from STV v1.0.2, after applying the 'STV::cleanBallots' method to remove the ten empty rows.

Usage

data(yale_ballots)

Format

A data frame with 479 observations and 44 candidates.