elect_algebra: Election Algebra for 2x2 Case

Description

Creates data.frame() table of algebraically defined white/non-white preferences for candidates. Typically used when analyst has high confidence in white turnout and voting behavior but needs to deduce minority voting behavior when only CVAP available. First, estimate white/non-white turnout using ei/rxc. Second, gather overall CVAP numbers. Third, estimate candidate preference by white/non-white using ei/rxc. Then enter values into function.

Usage

elect_algebra(totals, c1_ei_res, c2_ei_res, cand_names)

Arguments

totals

data.frame(), dimensions 2x2. Row 1 is white, row 2 is minority. First column is turnout (probably estimated from ei or rxc; e.g.: c(.2876, .1529)); second column is Citizen Voting Age Population (CVAP); e.g.: c(36472, 23851)

c1_ei_res

numeric vector of 2x2 EI candidate results by white voters, estimated from ei or rxc; e.g. c(0.2796, 0.7204) = whites voted 28% for candidate-a and 72% for candidate-b

c2_ei_res

numeric vector of 2x2 EI candidate results by non-white voters, estimated from ei or rxc

cand_names

Character vector of candidate names used for output, e.g.: c("Collingwood", "Barreto")

Value

Table with estimated candidate A/B votes by race, with columns for percent vote too

Details

elect_algebra

Examples

Run this code

# NOT RUN {
toy <- data.frame(
precinct = 1:10,
cvap_white = c(3669, 3349, 5726, 5229, 3862, 2079, 6109, 2098, 2397, 1954),
cvap_non_white = c(398, 2313, 449, 176, 3138, 6887, 3987, 831, 1493, 4179),
voted = c(1028, 829, 2350, 1473, 2552, 1029, 2207, 723, 1053, 878),
novote = c(3039, 4833, 3825, 3932, 4448, 7937, 7889, 2206, 2837, 5255),
total = c(4067, 5662, 6175, 5405, 7000, 8966, 10096, 2929, 3890, 6133),
pct_voted = c(0.2527662, 0.1464147, 0.3805668, 0.2725254, 0.3645714, 
             0.1147669, 0.2186014, 0.2468419, 0.2706941, 0.1431600),
pct_novote = c(0.7472338, 0.8535853, 0.6194332, 0.7274746, 0.6354286, 
              0.8852331, 0.7813986, 0.7531581, 0.7293059, 0.8568400),
pct_white = c(0.9021392, 0.5914871, 0.9272874, 0.9674376, 0.5517143, 
             0.2318760, 0.6050911, 0.7162854, 0.6161954, 0.3186043),
pct_nonwhite = c(0.0978608, 0.4085129, 0.0727126, 0.0325624, 0.4482857, 
                0.7681240, 0.3949089, 0.2837146, 0.3838046, 0.6813957),
cand_a = c(326, 745, 46, 66, 620, 830, 534, 388, 792, 617),
cand_b = c(702, 84, 2304, 1407, 1932, 199, 1673, 335, 261, 261),
pct_cand_a_voters = c(0.31712062, 0.89867310, 0.01957447, 0.04480652, 
                     0.24294671, 0.80660836, 0.24195741, 0.53665284, 
                     0.75213675, 0.70273349),
pct_cand_b_voters = c(0.6828794, 0.1013269, 0.9804255, 0.9551935, 0.7570533, 
                     0.1933916, 0.7580426, 0.4633472, 0.2478632, 0.2972665)
)

# NOT RUN: Estimate white/non-white Turnout #
#summary(ei_rxc(data = toy,
#        cand_cols = c("pct_voted","pct_novote"),
#        race_cols = c("pct_white", "pct_nonwhite"),
#        totals = "total", 
#        seed = 973472)
#        )

# Turnout by Race, Estimated: 27-28% White Turnout; 16-17% Minority Turnout
# Citizen Voting Age Population for Whole Jurisdiction; White, Non-White
totals <- data.frame(turnout = c(0.2786, 0.1663), cvap = c(36472, 23851))

# Not Run: Estimate Vote Choice 
# set.seed(197485)
#summary(ei_rxc(data = toy,
#       cand_cols = c("pct_cand_a_voters", "pct_cand_b_voters"),
#       race_cols = c("pct_white", "pct_nonwhite"),
#       totals_col = "total") 
#       )
#Extract Results 
c1_ei_res <- c(0.2796, 0.7204)
c2_ei_res <- c(0.7013, 0.2987)
#Set up vectors for function #
cand_names <- c("Cand A", "Cand B")
# Execute elect_algebra()
elect_algebra(totals = totals, c1_ei_res, c2_ei_res, cand_names)

# }

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