instr_logistic: Estimate Effect Using Instrument Variables via Logistic Fit

In FrankLef/fciR: Data and functions used with Fundamentals of Causal Inference by Babette Brumback

Estimate Effect Using Instrument Variables via Logistic Fit

Description

Estimate effect using instrument variables via logistic fit.

Usage

instr_logistic(
  data,
  formula,
  exposure.name,
  instrument.name,
  tol = .Machine$double.eps^0.5,
  niter = 10L
)

ivlogit.r(
  data,
  formula,
  exposure.name,
  instrument.name,
  tol = .Machine$double.eps^0.5,
  niter = 10L
)

Arguments

data	Dataframe of raw data.
formula	Formula representing the model. It must be a saturated model, e.g. Y ~ T * A.
exposure.name	Name of exposure variable.
instrument.name	Name of instrument variable.
tol	Numeric > 0. Tolerance used in estimation. Default is .Machine$double.eps^0.5.
niter	Number of iterations

Details

See section 9.3 p. 167-168 for details. It is important to note that the formula must specify a saturated model, e.g. Y ~ T * A.

Value

Dataframe in a useable format for rsample::bootstraps.

Source

Section 9.3

Examples

# An example can be found in the location identified in the
# source section above at the github site
# https://github.com/FrankLef/FundamentalsCausalInference.

FrankLef/fciR documentation built on Nov. 12, 2023, 6:09 a.m.