blocs (version 0.1.1)

vb_continuous: Continuous voting bloc analysis

Description

Define voting blocs along a continuous variable and estimate their partisan vote contributions.

Usage

vb_continuous(
  data,
  data_density = data,
  data_turnout = data,
  data_vote = data,
  indep,
  dv_vote3,
  dv_turnout,
  weight = NULL,
  min_val = NULL,
  max_val = NULL,
  n_points = 100,
  boot_iters = FALSE,
  verbose = FALSE,
  tolerance = sqrt(.Machine$double.eps),
  ...
)

Value

a vbdf data.frame with columns for the resample, bloc variable, and, for each resample-bloc combination, four estimates: probability density, turnout, Republican vote choice conditional on turnout, and net Republican votes.

Arguments

data

default data.frame to use as the source for density, turnout, and vote choice data.

data_density

data.frame of blocs' composition/density data. Must include any columns named by indep and weight.

data_turnout

data.frame of blocs' turnout data. Must include any columns named by dv_turnout, indep and weight.

data_vote

data.frame of blocs' vote choice data. Must include any columns named by dv_vote3, indep, and weight.

indep

string, column name of the independent variable defining discrete voting blocs.

dv_vote3

string, column name of the dependent variable in data_vote, coded as follows: -1 for Democrat vote choice, 0 for third-party vote, 1 for Republican vote choice, and NA for no vote.

dv_turnout

string, column name of the dependent variable flagging voter turnout in data_turnout. That column must be coded 0 = no vote, 1 = voted.

weight

optional string naming the column of sample weights.

min_val

numeric vector of the same length as indep, Lower bound for the density estimation of each respective indep. See [estimate_density].

max_val

numeric vector of the same length as indep, Upper bound for the density estimation of each respective indep. See [estimate_density].

n_points

scalar, number of points at which to estimate density. See [estimate_density].

boot_iters

integer, number of bootstrap iterations for uncertainty estimation. The default FALSE is equivalent to 0 and does not estimate uncertainty.

verbose

logical, whether to print iteration number.

tolerance

tolerance used when checking range of probability estimates

...

further arguments to pass to kde for density estimation.