Description Usage Arguments Details Value Author(s) References Examples

View source: R/iv_finite_factorial.R

Estimate main effect IV ratios for 2^K factorial experiments

1 | ```
iv_finite_factorial(formula, data, subset, level = 0.95)
``` |

`formula` |
formula specification of the factorial design with
noncompliance. The right-hand side of the formula should have
two components separated by the |

`data` |
a data.frame on which to apply the |

`subset` |
subset of the data to pass to estimation. |

`level` |
the confidence level required. |

This function estimates the ratio of the effect of treatment assignment on the outcome to the effect of treatment assignment on treatment uptake in 2^K factorial experiments. The approach uses finite sample asymptotic inference to generate confidence intervals.

A list of class `iv_finite_factorial`

that contains the
following components:

`tau` |
a vector of estimated effect ratios for each factor. |

`tau_cis` |
a matrix of confidence intervals for each effect ratio. This matrix has 4 columns because it is possible to have disjoint confidence intervals in this method. |

`tau_y` |
a vector of the estimated effects on the outcome. |

`v_tau_y` |
the estimated sample variances of the effects on the outcome. |

`tau_d` |
a vector of the estimated effects on treatment uptake. |

`v_tau_y` |
the estimated sample variances of the effects on treatment uptake. |

`level` |
the confidence level of |

Matt Blackwell

Matthew Blackwell and Nicole Pashley (2021) "Noncompliance in Factorial Experiments." Working paper.

1 2 3 4 5 6 7 8 9 10 11 | ```
data(newhaven)
out <- iv_finite_factorial(turnout_98 ~ inperson + phone | inperson_rand
+ phone_rand, data = newhaven)
out
joint <- iv_finite_factorial(turnout_98 ~ inperson + phone |
inperson_rand + phone_rand, data = newhaven)
joint
``` |

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