Description Usage Arguments Value Author(s) References See Also Examples
rdplot
implements several datadriven Regression Discontinuity (RD) plots, using either evenlyspaced or quantilespaced partitioning. Two type of RD plots are constructed: (i) RD plots with binned sample means tracing out the underlying regression function, and (ii) RD plots with binned sample means mimicking the underlying variability of the data. For technical and methodological details see Calonico, Cattaneo and Titiunik (2015a).
Companion commands are: rdrobust
for point estimation and inference procedures, and rdbwselect
for datadriven bandwidth selection.
A detailed introduction to this command is given in Calonico, Cattaneo and Titiunik (2015b) and Calonico, Cattaneo, Farrell and Titiunik (2017). A companion Stata
package is described in Calonico, Cattaneo and Titiunik (2014).
For more details, and related Stata and R packages useful for analysis of RD designs, visit https://rdpackages.github.io/
1 2 3 4 5 6 7  rdplot(y, x, c = 0, p = 4, nbins = NULL, binselect = "esmv",
scale = NULL, kernel = "uni", weights = NULL, h = NULL,
covs = NULL, covs_eval = "mean", covs_drop = TRUE, ginv.tol = 1e20,
support = NULL, subset = NULL, masspoints = "adjust",
hide = FALSE, ci = NULL, shade = FALSE, title = NULL,
x.label = NULL, y.label = NULL, x.lim = NULL, y.lim = NULL,
col.dots = NULL, col.lines = NULL)

y 
is the dependent variable. 
x 
is the running variable (a.k.a. score or forcing variable). 
c 
specifies the RD cutoff in 
p 
specifies the order of the globalpolynomial used to approximate the population conditional mean functions for control and treated units; default is 
nbins 
specifies the number of bins used to the left of the cutoff, denoted J_, and to the right of the cutoff, denoted J_+, respectively. If not specified, J_+ and J_ are estimated using the method and options chosen below. 
binselect 
specifies the procedure to select the number of bins. This option is available only if J_ and J_+ are not set manually. Options are:

scale 
specifies a multiplicative factor to be used with the optimal numbers of bins selected. Specifically, the number of bins used for the treatment and control groups will be 
kernel 
specifies the kernel function used to construct the localpolynomial estimator(s). Options are: 
weights 
is the variable used for optional weighting of the estimation procedure. The unitspecific weights multiply the kernel function. 
h 
specifies the bandwidth used to construct the (global) polynomial fits given the kernel choice 
covs 
specifies additional covariates to be used in the polynomial regression. 
covs_eval 
sets the evaluation points for the additional covariates, when included in the estimation. Options are: 
covs_drop 
if TRUE, it checks for collinear additional covariates and drops them. Default is TRUE. 
ginv.tol 
tolerance used to invert matrices involving covariates when 
support 
specifies an optional extended support of the running variable to be used in the construction of the bins; default is the sample range. 
subset 
an optional vector specifying a subset of observations to be used. 
masspoints 
checks and controls for repeated observations in the running variable. Options are: (i) (ii) (iii) Default option is 
hide 
logical. If 
ci 
optional graphical option to display confidence intervals of selected level for each bin. 
shade 
optional graphical option to replace confidence intervals with shaded areas. 
title 
optional title for the RD plot. 
x.label 
optional label for the xaxis of the RD plot. 
y.label 
optional label for the yaxis of the RD plot. 
x.lim 
optional setting for the range of the xaxis in the RD plot. 
y.lim 
optional setting for the range of the yaxis in the RD plot. 
col.dots 
optional setting for the color of the dots in the RD plot. 
col.lines 
optional setting for the color of the lines in the RD plot. 
binselect 
method used to compute the optimal number of bins. 
N 
sample sizes used to the left and right of the cutoff. 
Nh 
effective sample sizes used to the left and right of the cutoff. 
c 
cutoff value. 
p 
order of the global polynomial used. 
h 
bandwidth used to the left and right of the cutoff. 
kernel 
kernel used. 
J 
selected number of bins to the left and right of the cutoff. 
J_IMSE 
IMSE optimal number of bins to the left and right of the cutoff. 
J_MV 
Mimicking variance number of bins to the left and right of the cutoff. 
coef 
matrix containing the coefficients of the p^{th} order global polynomial estimated both sides of the cutoff. 
scale 
selected scale value. 
rscale 
implicit scale value. 
bin_avg 
average bin length. 
bin_med 
median bin length. 
vars_bins 
data frame containing the variables used to construct the bins: bin id, cutoff values, mean of x and y within each bin, cutoff points and confidence interval bounds. 
vars_poly 
data frame containing the variables used to construct the global polynomial plot. 
rdplot 
a standard 
Sebastian Calonico, Columbia University, New York, NY. sebastian.calonico@columbia.edu.
Matias D. Cattaneo, Princeton University, Princeton, NJ. cattaneo@princeton.edu.
Max H. Farrell, University of Chicago, Chicago, IL. max.farrell@chicagobooth.edu.
Rocio Titiunik, Princeton University, Princeton, NJ. titiunik@princeton.edu.
Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik. 2017. rdrobust: Software for Regression Discontinuity Designs. Stata Journal 17(2): 372404.
Calonico, S., M. D. Cattaneo, and R. Titiunik. 2014. Robust DataDriven Inference in the RegressionDiscontinuity Design. Stata Journal 14(4): 909946.
Calonico, S., M. D. Cattaneo, and R. Titiunik. 2015a. Optimal DataDriven Regression Discontinuity Plots. Journal of the American Statistical Association 110(512): 17531769.
Calonico, S., M. D. Cattaneo, and R. Titiunik. 2015b. rdrobust: An R Package for Robust Nonparametric Inference in RegressionDiscontinuity Designs. R Journal 7(1): 3851.
Cattaneo, M. D., B. Frandsen, and R. Titiunik. 2015. Randomization Inference in the Regression Discontinuity Design: An Application to the Study of Party Advantages in the U.S. Senate. Journal of Causal Inference 3(1): 124.
1 2 3 
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.