tip_coef_with_r2: Tip a regression coefficient using the partial R2 for an unmeasured confounder-exposure relationship and unmeasured confounder- outcome relationship

Description

Choose one of the following, and the other will be estimated:

confounder_exposure_r2
confounder_outcome_r2

Usage

tip_coef_with_r2(
  effect_observed,
  se,
  df,
  confounder_exposure_r2 = NULL,
  confounder_outcome_r2 = NULL,
  verbose = TRUE,
  alpha = 0.05,
  tip_bound = FALSE,
  ...
)

Value

A data frame.

Arguments

effect_observed: Numeric. Observed exposure - outcome effect from a regression model. This is the point estimate (beta coefficient)
se: Numeric. Standard error of the effect_observed in the previous parameter.
df: Numeric positive value. Residual degrees of freedom for the model used to estimate the observed exposure - outcome effect. This is the total number of observations minus the number of parameters estimated in your model. Often for models estimated with an intercept this is N - k - 1 where k is the number of predictors in the model.
confounder_exposure_r2: Numeric value between 0 and 1. The assumed partial R2 of the unobserved confounder with the exposure given the measured covariates.
confounder_outcome_r2: Numeric value between 0 and 1. The assumed partial R2 of the unobserved confounder with the outcome given the exposure and the measured covariates.
verbose: Logical. Indicates whether to print informative message. Default: TRUE
alpha: Significance level. Default = 0.05.
tip_bound: Do you want to tip at the bound? Default = FALSE, will tip at the point estimate
...: Optional arguments passed to the sensemakr::adjusted_estimate() function.

Examples

Run this code

tip_coef_with_r2(0.5, 0.1, 102, 0.5)

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