generateMarginalEffect: Generate Marginal Effects

In zieglerjef/openEnded: Tools for Implementing and Analyzing Open-Ended Responses as Manipulation Checks

Description

Internal function that helps calculate the overall treatment effects differing how inattentive participants are down-weighted.

Usage

generateMarginalEffect(
  unique_covars,
  simulated_betas,
  diff_labs,
  model_type,
  plotDifferences
)

Arguments

unique_covars	Model matrix of unique characteristics used to generate treatment effects.
simulated_betas	−XBetas that have been simulated from mvrnorm distribution
diff_labs
model_type	Statistical model to estimate. Currently support OLS and logistic ("ls", "logit").
plotDifferences	Do you want to see the marginal effects by model, or the differences between the models with regard to their marginal effects? Default=FALSE.

Value

Dataframe of marginal effects with corresponding 95

Author(s)

Jeffrey Ziegler (<jeffrey.ziegler[at]emory.edu>)

See Also

regressionComparison

Examples

generateMarginalEffect(generateMarginalEffect(unique_covars = unique_dummies, 
simulated_betas=sim_betas, diff_labs=fd_labs[,1]))

zieglerjef/openEnded documentation built on Nov. 30, 2020, 2:03 p.m.