konfound_glm_dichotomous: Konfound Analysis for Generalized Linear Models with...

In jrosen48/konfound: Quantify the Robustness of Causal Inferences

Konfound Analysis for Generalized Linear Models with Dichotomous Outcomes

Description

This function performs konfound analysis on a generalized linear model object with a dichotomous outcome. It uses 'broom' to tidy model outputs and calculates the sensitivity of inferences.

Usage

konfound_glm_dichotomous(
  model_object,
  tested_variable_string,
  alpha,
  tails,
  to_return,
  n_treat,
  switch_trm,
  replace
)

Arguments

model_object	The model object produced by glm.
tested_variable_string	The name of the variable being tested.
alpha	Significance level for hypothesis testing.
tails	Number of tails for the test (1 or 2).
to_return	The type of output to return.
n_treat	Number of treatment cases.
switch_trm	Term to switch for sensitivity analysis.
replace	Boolean indicating whether to replace cases or not.

Value

The results of the konfound analysis.

jrosen48/konfound documentation built on April 13, 2024, 3:47 a.m.