rdplot | R Documentation |
rdplot
implements several data-driven Regression Discontinuity (RD) plots, using either evenly-spaced or quantile-spaced 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 data-driven 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/
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 = 1e-20,
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 global-polynomial 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 |
binselect |
specifies the procedure to select the number of bins. This option is available only if
|
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 local-polynomial estimator(s). Options are: |
weights |
is the variable used for optional weighting of the estimation procedure. The unit-specific 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 x-axis of the RD plot. |
y.label |
optional label for the y-axis of the RD plot. |
x.lim |
optional setting for the range of the x-axis in the RD plot. |
y.lim |
optional setting for the range of the y-axis 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 |
coef_covs |
coefficients of the additional covariates, only returned when |
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 California, Santa Barbara, CA. maxhfarrell@ucsb.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): 372-404.
Calonico, S., M. D. Cattaneo, and R. Titiunik. 2014. Robust Data-Driven Inference in the Regression-Discontinuity Design. Stata Journal 14(4): 909-946.
Calonico, S., M. D. Cattaneo, and R. Titiunik. 2015a. Optimal Data-Driven Regression Discontinuity Plots. Journal of the American Statistical Association 110(512): 1753-1769.
Calonico, S., M. D. Cattaneo, and R. Titiunik. 2015b. rdrobust: An R Package for Robust Nonparametric Inference in Regression-Discontinuity Designs. R Journal 7(1): 38-51.
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): 1-24.
rdbwselect
, rdrobust
x<-runif(1000,-1,1)
y<-5+3*x+2*(x>=0)+rnorm(1000)
rdplot(y,x)
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