estimate_density

Run kde for weighted density estimation
of a <code>x</code> at <code>n_points</code>
evenly spaced points between <code>min</code> and max.

Functions to combine data on voting blocs'
size, turnout, and vote choice to estimate each bloc's
vote contributions to the Democratic and Republican parties.
The package also includes functions for uncertainty estimation and
plotting. Users may define voting blocs along a discrete or continuous variable.
The package implements methods described in Grimmer, Marble, and Tanigawa-Lau (2023) <doi:10.31235/osf.io/c9fkg>.

Cole Tanigawa-Lau

blocs

Estimate and Visualize Voting Blocs' Partisan Contributions

Justin Grimmer

Will Marble

estimate_density function

<dl><dt>x</dt>
<dd>numeric vector or matrix</dd>
<dt>min</dt>
<dd>numeric vector giving the lower bound of evaluation points for each variable in <code>x</code></dd>
<dt>max</dt>
<dd>numeric vector giving the upper bound of evaluation points for each variable in <code>x</code></dd>
<dt>n_points</dt>
<dd>number of evaluation points (estimates)</dd>
<dt>w</dt>
<dd>vector of weights. Default uses uniform weighting.</dd>
<dt>...</dt>
<dd>further arguments to pass to kde</dd></dl>

Arguments

Estimate density — estimate_density

<dl>

<dt>x</dt>
<dd>numeric vector or matrix</dd>


<dt>min</dt>
<dd>numeric vector giving the lower bound of evaluation points for each variable in <code>x</code></dd>


<dt>max</dt>
<dd>numeric vector giving the upper bound of evaluation points for each variable in <code>x</code></dd>


<dt>n_points</dt>
<dd>number of evaluation points (estimates)</dd>


<dt>w</dt>
<dd>vector of weights. Default uses uniform weighting.</dd>


<dt>...</dt>
<dd>further arguments to pass to kde</dd>

</dl>

estimate_density: Estimate density

Description

Usage

Arguments