plotComplierATE: Simulate the ATE for Compliers and Non-Compliers

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

Description

Plot the marginal effects for respondents that likely received and did not receive the treatment.

Usage

complierATE(
  dataframe = NULL,
  formula = NULL,
  plot_treatment = NULL,
  plot_interact_x = NULL,
  similarity_measures = c("jaccardDist", "cosineDist"),
  bounds = c(0.1, 0.2),
  n = 100,
  user_seed = 5,
  model_type = NULL,
  k = 3,
  display_plot = T,
  plot_path = NULL,
  stable_x,
  k_plot = F,
  return_data = F
)

Arguments

dataframe	Dataframe from which we will estimate our regression model.
formula	Symbolic representation of the model to be estimated. This is written in "typical" R language (i.e. y ~ x1 + x2), such that y is the outcome variable and x1 and x2 are the predictors.
similarity_measures	Vector(s) from dataframe that contains the similarity measures to be used as weights. Possible values for measure_type = c("osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw", "soundex"). Default is "jaccard".
bounds	Minimum and maximum of uniform distribution we should draw cutoff values between.
n	Number of simulation rounds/iterations.
model_type	Statistical model to estimate. Currently support OLS and logistic ("ls", "logit").
k	The penalty that you want to set for down-weighting inattentive respondents. Lower levels of k down-weight low attention participants more severely.
k_plot	Do you want to see a histogram of the cutoffs used in the simulations?
return_data	Do you want the data that's used to construct the plot? Default = FALSE.

Value

Plot of the marginal effects for "compliers" and "non-compliers".

Author(s)

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

Examples

plotComplierATE(dataframe=replication_complete.cases, similarity_measures=c("jaccardDist", "cosineDist"), bounds=c(0.05, 0.2), n=2, seed=12345, k=3, formula=list(trustLM, responsiveLM), model_type="ls")

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