# cdesens: Estimate sensitivity of ACDE estimates under varying levels... In DirectEffects: Estimating Controlled Direct Effects for Explaining Causal Findings

## Description

Estimate how the Average Controlled Direct Effect varies by various levels of unobserved confounding. For each value of unmeasured confounding, summarized as a correlation between residuals, cdesens computes the ACDE. Standard errors are computed by a simple bootstrap.

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```cdesens( seqg, var, rho = seq(-0.9, 0.9, by = 0.05), bootstrap = c("none", "standard"), boots_n = 1000, verbose = FALSE, ... ) ```

## Arguments

 `seqg` Output from sequential_g. The function only supports specifications with one mediator variable. `var` A character indicating the name of the variable for which the estimated ACDE is being evaluated. `rho` A numerical vector of correlations between errors to test for. The original model assumes rho = 0 `bootstrap` character of c("none", "standard"), indicating whether to include bootstrap standard errors. Default is "none". `boots_n` Number of bootstrap replicates, defaults to 100. `verbose` Whether to show progress and messages, defaults to FALSE `...` Other parameters to pass on to lm.fit() when refitting the model

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```data(civilwar) # main formula: Y ~ A + X | Z | M form_main <- onset ~ ethfrac + lmtnest + ncontig + Oil | warl + gdpenl + lpop + polity2l + relfrac | instab # estimate CDE direct <- sequential_g(form_main, data = civilwar) # sensitivity out_sens <- cdesens(direct, var = "ethfrac") # plot sensitivity plot(out_sens) ```

DirectEffects documentation built on May 13, 2021, 1:08 a.m.