Continuous voting bloc analysis
vb_continuous.RdDefine 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),
...
)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
indepandweight.- data_turnout
data.frame of blocs' turnout data. Must include any columns named by
dv_turnout,indepandweight.- data_vote
data.frame of blocs' vote choice data. Must include any columns named by
dv_vote3,indep, andweight.- 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 respectiveindep. See [estimate_density].- max_val
numeric vector of the same length as
indep, Upper bound for the density estimation of each respectiveindep. 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
FALSEis 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.