backdr_out_sat: Standardized estimates via Outcome Modeling, Saturated...

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

Standardized estimates via Outcome Modeling, Saturated regression

Description

Standardized estimates via outcome modeling, saturated regression.

Usage

backdr_out_sat(data, formula, exposure.name, confound.names, att = FALSE)

stand.r(data, formula, exposure.name, confound.names, att = FALSE)

Arguments

data	Dataframe of raw data.
formula	Formula representing the model.
exposure.name	Name of exposure variable.
confound.names	Names of the confound variables.
att	if FALSE calculate the standardized (unconfounded) causal effect. If TRUE calculate the average effect of treatment on the treated.

Details

The standardized estimates are computed using a saturated regression fit. That is all variables and their interactions are used. make sure all variables and their interactions are included in the formula. See p. 101.

Value

Dataframe in a useable format for rsample::bootstraps.

Source

Section 6.1

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.