meas_effect_cond: Compute estimates of the conditional association measures

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

Compute estimates of the conditional association measures

Description

Compute estimates of the conditional association measures.

Usage

meas_effect_cond(
  data,
  formula,
  exposure.name,
  confound.names,
  condition.names = confound.names,
  family = c("binomial", "poisson", "gaussian")
)

bootc(
  data,
  formula,
  exposure.name,
  confound.names,
  condition.names = confound.names,
  family = c("binomial", "poisson", "gaussian")
)

Arguments

data	Dataframe of raw data.
formula	The model formula.
exposure.name	Name of the exposure variable.
confound.names	Name of confound variables.
condition.names	Character vector of conditioned variable names. By default, it will be the same as the confound.names.
family	Name of distribution. Must be in c("binomial", "poisson", "gaussian").

Details

Estimate the expected conditional outcomes and the conditional effect or association measures. See exercise 3 of chapter 3 for a full example on how to use this function.

Value

Dataframe in a useable format for rsample::bootstraps.

Source

Section 3.3

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