vb_continuous {blocs} | R Documentation |

## 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),
...
)
```

### 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 |

`data_turnout` |
data.frame of blocs' turnout data. Must include any
columns named by |

`data_vote` |
data.frame of blocs' vote choice data. Must include any
columns named by |

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

`dv_vote3` |
string, column name of the dependent variable in |

`dv_turnout` |
string, column name of the dependent variable flagging
voter turnout in |

`weight` |
optional string naming the column of sample weights. |

`min_val` |
numeric vector of the same length as |

`max_val` |
numeric vector of the same length as |

`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 |

`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. |

### 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.

*blocs*version 0.1.1 Index]