R/konfound-lm.R

In jrosen48/rsensitivity: Quantify the Robustness of Causal Inferences

# konfound-lm

#' Konfound Analysis for Linear Models
#'
#' This function performs konfound analysis on a linear model object
#' produced by lm.
#' It calculates the sensitivity of inferences for coefficients in the model.
#' It supports analysis for a single variable or multiple variables.
#'
#' @param model_object The linear model object produced by lm.
#' @param tested_variable_string The name of the variable being tested.
#' @param alpha Significance level for hypothesis testing.
#' @param tails Number of tails for the test (1 or 2).
#' @param index Type of sensitivity analysis ('RIR' by default).
#' @param to_return The type of output to return.
#' @return The results of the konfound analysis for the specified variable(s).
#' @importFrom broom tidy glance
#' @importFrom dplyr select filter bind_cols
#' @importFrom stats var
konfound_lm <- function(model_object,
                        tested_variable_string,
                        alpha,
                        tails,
                        index,
                        to_return) {
    tidy_output <- broom::tidy(model_object) # tidying output
    glance_output <- broom::glance(model_object)

    coef_df <- tidy_output[tidy_output$term == tested_variable_string, ]
    sdx <- unname(sqrt(diag(stats::var(model_object$model)))[tested_variable_string])

  est_eff <- coef_df$estimate
  std_err <- coef_df$std.error
  n_obs <- glance_output$nobs
  n_covariates <- unname(glance_output$df)
  sdy <- unname(sqrt(diag(var(model_object$model)))[1])
  R2 <- summary(model_object)$r.squared

  out <- test_sensitivity(
      est_eff = est_eff,
      std_err = std_err,
      n_obs = n_obs,
      n_covariates = n_covariates - 1,
      sdx = sdx,
      sdy = sdy,
      R2 = R2,
      alpha = alpha,
      tails = tails,
      index = index,
      nu = 0,
      far_bound = 0,
      eff_thr = NA,
      to_return = to_return,
      model_object = model_object,
      tested_variable = tested_variable_string
  )

    return(out)
}