Package 'rdlocrand' reference manual

Title:	Local Randomization Methods for RD Designs
Description:	The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. Under the local randomization approach, RD designs can be interpreted as randomized experiments inside a window around the cutoff. This package provides tools to perform randomization inference for RD designs under local randomization: rdrandinf() to perform hypothesis testing using randomization inference, rdwinselect() to select a window around the cutoff in which randomization is likely to hold, rdsensitivity() to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds() to construct Rosenbaum bounds for sensitivity to unobserved confounders. See Cattaneo, Titiunik and Vazquez-Bare (2016) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2016_Stata.pdf> for further methodological details.
Authors:	Matias D. Cattaneo, Rocio Titiunik, Gonzalo Vazquez-Bare
Maintainer:	Gonzalo Vazquez-Bare <[email protected]>
License:	GPL-2
Version:	1.0
Built:	2025-03-02 04:04:23 UTC
Source:	https://github.com/cran/rdlocrand

rdlocrand: Local Randomization Methods for RD Designs

Description

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. Under the local randomization approach, RD designs can be interpreted as randomized experiments inside a window around the cutoff. The rdlocrand package provides tools to analyze RD designs under local randomization: rdrandinf to perform hypothesis testing using randomization inference, rdwinselect to select a window around the cutoff in which randomization is likely to hold, rdsensitivity to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds to construct Rosenbaum bounds for sensitivity to unobserved confounders. For more details, and related Stata and R packages useful for analysis of RD designs, visit https://rdpackages.github.io/.

Author(s)

Matias Cattaneo, Princeton University. [email protected]

Rocio Titiunik, Princeton University. [email protected]

Gonzalo Vazquez-Bare, UC Santa Barbara. [email protected]

References

Cattaneo, M.D., B. Frandsen and R. Titiunik. (2015). Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate. Journal of Causal Inference 3(1): 1-24.

Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2016). Inference in Regression Discontinuity Designs under Local Randomization. Stata Journal 16(2): 331-367.

Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2017). Comparing Inference Approaches for RD Designs: A Reexamination of the Effect of Head Start on Child Mortality. Journal of Policy Analysis and Management 36(3): 643-681.

Rosenbaum, P. (2002). Observational Studies. Springer.

Randomization Inference for RD Designs under Local Randomization

Description

rdrandinf implements randomization inference and related methods for RD designs, using observations in a specified or data-driven selected window around the cutoff where local randomization is assumed to hold.

Usage

rdrandinf(
  Y,
  R,
  cutoff = 0,
  wl = NULL,
  wr = NULL,
  statistic = "diffmeans",
  p = 0,
  evall = NULL,
  evalr = NULL,
  kernel = "uniform",
  fuzzy = NULL,
  nulltau = 0,
  d = NULL,
  dscale = NULL,
  ci,
  interfci = NULL,
  bernoulli = NULL,
  reps = 1000,
  seed = 666,
  quietly = FALSE,
  covariates,
  obsmin = NULL,
  wmin = NULL,
  wobs = NULL,
  wstep = NULL,
  wasymmetric = FALSE,
  wmasspoints = FALSE,
  nwindows = 10,
  dropmissing = FALSE,
  rdwstat = "diffmeans",
  approx = FALSE,
  rdwreps = 1000,
  level = 0.15,
  plot = FALSE,
  firststage = FALSE,
  obsstep = NULL
)
rdrandinf(
  Y,
  R,
  cutoff = 0,
  wl = NULL,
  wr = NULL,
  statistic = "diffmeans",
  p = 0,
  evall = NULL,
  evalr = NULL,
  kernel = "uniform",
  fuzzy = NULL,
  nulltau = 0,
  d = NULL,
  dscale = NULL,
  ci,
  interfci = NULL,
  bernoulli = NULL,
  reps = 1000,
  seed = 666,
  quietly = FALSE,
  covariates,
  obsmin = NULL,
  wmin = NULL,
  wobs = NULL,
  wstep = NULL,
  wasymmetric = FALSE,
  wmasspoints = FALSE,
  nwindows = 10,
  dropmissing = FALSE,
  rdwstat = "diffmeans",
  approx = FALSE,
  rdwreps = 1000,
  level = 0.15,
  plot = FALSE,
  firststage = FALSE,
  obsstep = NULL
)

Arguments

`Y`	a vector containing the values of the outcome variable.
`R`	a vector containing the values of the running variable.
`cutoff`	the RD cutoff (default is 0).
`wl`	the left limit of the window. The default takes the minimum of the running variable.
`wr`	the right limit of the window. The default takes the maximum of the running variable.
`statistic`	the statistic to be used in the balance tests. Allowed options are `diffmeans` (difference in means statistic), `ksmirnov` (Kolmogorov-Smirnov statistic) and `ranksum` (Wilcoxon-Mann-Whitney standardized statistic). Default option is `diffmeans`. The statistic `ttest` is equivalent to `diffmeans` and included for backward compatibility.
`p`	the order of the polynomial for outcome transformation model (default is 0).
`evall`	the point at the left of the cutoff at which to evaluate the transformed outcome is evaluated. Default is the cutoff value.
`evalr`	specifies the point at the right of the cutoff at which the transformed outcome is evaluated. Default is the cutoff value.
`kernel`	specifies the type of kernel to use as weighting scheme. Allowed kernel types are `uniform` (uniform kernel), `triangular` (triangular kernel) and `epan` (Epanechnikov kernel). Default is `uniform`.
`fuzzy`	indicates that the RD design is fuzzy. `fuzzy` can be specified as the variable containing the values of the endogenous treatment variable, or as a vector where the first element is the vector of endogenous treatment values and the second element is a string containing the name of the statistic to be used. Allowed statistics are `itt` (intention-to-treat statistic) and `tsls` (2SLS statistic). Default statistic is `ar`. The `tsls` statistic relies on large-sample approximation.
`nulltau`	the value of the treatment effect under the null hypothesis (default is 0).
`d`	the effect size for asymptotic power calculation. Default is 0.5 * standard deviation of outcome variable for the control group.
`dscale`	the fraction of the standard deviation of the outcome variable for the control group used as alternative hypothesis for asymptotic power calculation. Default is 0.5.
`ci`	calculates a confidence interval for the treatment effect by test inversion. `ci` can be specified as a scalar or a vector, where the first element indicates the value of alpha for the confidence interval (typically 0.05 or 0.01) and the remaining elements, if specified, indicate the grid of treatment effects to be evaluated. This option uses `rdsensitivity` to calculate the confidence interval. See corresponding help for details. Note: the default tlist can be narrow in some cases, which may truncate the confidence interval. We recommend the user to manually set a large enough tlist.
`interfci`	the level for Rosenbaum's confidence interval under arbitrary interference between units.
`bernoulli`	the probabilities of treatment for each unit when assignment mechanism is a Bernoulli trial. This option should be specified as a vector of length equal to the length of the outcome and running variables.
`reps`	the number of replications (default is 1000).
`seed`	the seed to be used for the randomization test.
`quietly`	suppresses the output table.
`covariates`	the covariates used by `rdwinselect` to choose the window when `wl` and `wr` are not specified. This should be a matrix of size n x k where n is the total sample size and k is the number of covariates.
`obsmin`	the minimum number of observations above and below the cutoff in the smallest window employed by the companion command `rdwinselect`. Default is 10.
`wmin`	the smallest window to be used (if `minobs` is not specified) by the companion command `rdwinselect`. Specifying both `wmin` and `obsmin` returns an error.
`wobs`	the number of observations to be added at each side of the cutoff at each step.
`wstep`	the increment in window length (if `obsstep` is not specified) by the companion command `rdwinselect`. Specifying both `obsstep` and `wstep` returns an error.
`wasymmetric`	allows for asymmetric windows around the cutoff when (`wobs` is specified).
`wmasspoints`	specifies that the running variable is discrete and each masspoint should be used as a window.
`nwindows`	the number of windows to be used by the companion command `rdwinselect`. Default is 10.
`dropmissing`	drop rows with missing values in covariates when calculating windows.
`rdwstat`	the statistic to be used by the companion command `rdwinselect` (see corresponding help for options). Default option is `ttest`.
`approx`	forces the companion command `rdwinselect` to conduct the covariate balance tests using a large-sample approximation instead of finite-sample exact randomization inference methods.
`rdwreps`	the number of replications to be used by the companion command `rdwinselect`. Default is 1000.
`level`	the minimum accepted value of the p-value from the covariate balance tests to be used by the companion command `rdwinselect`. Default is .15.
`plot`	draws a scatter plot of the minimum p-value from the covariate balance test against window length implemented by the companion command `rdwinselect`.
`firststage`	reports the results from the first step when using tsls.
`obsstep`	the minimum number of observations to be added on each side of the cutoff for the sequence of fixed-increment nested windows. Default is 2. This option is deprecated and only included for backward compatibility.

Value

`sumstats`	summary statistics
`obs.stat`	observed statistic(s)
`p.value`	randomization p-value(s)
`asy.pvalue`	asymptotic p-value(s)
`window`	chosen window
`ci`	confidence interval (only if `ci` option is specified)
`interf.ci`	confidence interval under interferecen (only if `interfci` is specified)

Author(s)

Matias Cattaneo, Princeton University. [email protected]

Rocio Titiunik, Princeton University. [email protected]

Gonzalo Vazquez-Bare, UC Santa Barbara. [email protected]

References

Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2016). Inference in Regression Discontinuity Designs under Local Randomization. Stata Journal 16(2): 331-367.

Examples

# Toy dataset
X <- array(rnorm(200),dim=c(100,2))
R <- X[1,] + X[2,] + rnorm(100)
Y <- 1 + R -.5*R^2 + .3*R^3 + (R>=0) + rnorm(100)
# Randomization inference in window (-.75,.75)
tmp <- rdrandinf(Y,R,wl=-.75,wr=.75)
# Randomization inference in window (-.75,.75), all statistics
tmp <- rdrandinf(Y,R,wl=-.75,wr=.75,statistic='all')
# Randomization inference with window selection
# Note: low number of replications to speed up process.
# The user should increase the number of replications.
tmp <- rdrandinf(Y,R,statistic='all',covariates=X,wmin=.5,wstep=.125,rdwreps=500)



# Toy dataset
X <- array(rnorm(200),dim=c(100,2))
R <- X[1,] + X[2,] + rnorm(100)
Y <- 1 + R -.5*R^2 + .3*R^3 + (R>=0) + rnorm(100)
# Randomization inference in window (-.75,.75)
tmp <- rdrandinf(Y,R,wl=-.75,wr=.75)
# Randomization inference in window (-.75,.75), all statistics
tmp <- rdrandinf(Y,R,wl=-.75,wr=.75,statistic='all')
# Randomization inference with window selection
# Note: low number of replications to speed up process.
# The user should increase the number of replications.
tmp <- rdrandinf(Y,R,statistic='all',covariates=X,wmin=.5,wstep=.125,rdwreps=500)

Rosenbaum bounds for RD designs under local randomization

Description

rdrbounds calculates lower and upper bounds for the randomization p-value under different degrees of departure from a local randomized experiment, as suggested by Rosenbaum (2002).

Usage

rdrbounds(
  Y,
  R,
  cutoff = 0,
  wlist,
  gamma,
  expgamma,
  bound = "both",
  statistic = "ranksum",
  p = 0,
  evalat = "cutoff",
  kernel = "uniform",
  fuzzy = NULL,
  nulltau = 0,
  prob,
  fmpval = FALSE,
  reps = 1000,
  seed = 666
)
rdrbounds(
  Y,
  R,
  cutoff = 0,
  wlist,
  gamma,
  expgamma,
  bound = "both",
  statistic = "ranksum",
  p = 0,
  evalat = "cutoff",
  kernel = "uniform",
  fuzzy = NULL,
  nulltau = 0,
  prob,
  fmpval = FALSE,
  reps = 1000,
  seed = 666
)

Arguments

`Y`	a vector containing the values of the outcome variable.
`R`	a vector containing the values of the running variable.
`cutoff`	the RD cutoff (default is 0).
`wlist`	the list of window lengths to be evaluated. By default the program constructs 10 windows around the cutoff, the first one including 10 treated and control observations and adding 5 observations to each group in subsequent windows.
`gamma`	the list of values of gamma to be evaluated.
`expgamma`	the list of values of exp(gamma) to be evaluated. Default is `c(1.5,2,2.5,3)`.
`bound`	specifies which bounds the command calculates. Options are `upper` for upper bound, `lower` for lower bound and `both` for both upper and lower bounds. Default is `both`.
`statistic`	the statistic to be used in the balance tests. Allowed options are `diffmeans` (difference in means statistic), `ksmirnov` (Kolmogorov-Smirnov statistic) and `ranksum` (Wilcoxon-Mann-Whitney standardized statistic). Default option is `diffmeans`. The statistic `ttest` is equivalent to `diffmeans` and included for backward compatibility.
`p`	the order of the polynomial for outcome adjustment model. Default is 0.
`evalat`	specifies the point at which the adjusted variable is evaluated. Allowed options are `cutoff` and `means`. Default is `cutoff`.
`kernel`	specifies the type of kernel to use as weighting scheme. Allowed kernel types are `uniform` (uniform kernel), `triangular` (triangular kernel) and `epan` (Epanechnikov kernel). Default is `uniform`.
`fuzzy`	indicates that the RD design is fuzzy. `fuzzy` can be specified as a vector containing the values of the endogenous treatment variable, or as a list where the first element is the vector of endogenous treatment values and the second element is a string containing the name of the statistic to be used. Allowed statistics are `ar` (Anderson-Rubin statistic) and `tsls` (2SLS statistic). Default statistic is `ar`. The `tsls` statistic relies on large-sample approximation.
`nulltau`	the value of the treatment effect under the null hypothesis. Default is 0.
`prob`	the probabilities of treatment for each unit when assignment mechanism is a Bernoulli trial. This option should be specified as a vector of length equal to the length of the outcome and running variables.
`fmpval`	reports the p-value under fixed margins randomization, in addition to the p-value under Bernoulli trials.
`reps`	number of replications. Default is 1000.
`seed`	the seed to be used for the randomization tests.

Value

`gamma`	list of gamma values.
`expgamma`	list of exp(gamma) values.
`wlist`	window grid.
`p.values`	p-values for each window (under gamma = 0).
`lower.bound`	list of lower bound p-values for each window and gamma pair.
`upper.bound`	list of upper bound p-values for each window and gamma pair.

Author(s)

Matias Cattaneo, Princeton University. [email protected]

Rocio Titiunik, Princeton University. [email protected]

Gonzalo Vazquez-Bare, UC Santa Barbara. [email protected]

References

Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2016). Inference in Regression Discontinuity Designs under Local Randomization. Stata Journal 16(2): 331-367.

Rosenbaum, P. (2002). Observational Studies. Springer.

Examples

# Toy dataset
R <- runif(100,-1,1)
Y <- 1 + R -.5*R^2 + .3*R^3 + (R>=0) + rnorm(100)
# Rosenbaum bounds
# Note: low number of replications and windows to speed up process.
# The user should increase these values.
rdrbounds(Y,R,expgamma=c(1.5,2),wlist=c(.3),reps=100)


# Toy dataset
R <- runif(100,-1,1)
Y <- 1 + R -.5*R^2 + .3*R^3 + (R>=0) + rnorm(100)
# Rosenbaum bounds
# Note: low number of replications and windows to speed up process.
# The user should increase these values.
rdrbounds(Y,R,expgamma=c(1.5,2),wlist=c(.3),reps=100)

Sensitivity analysis for RD designs under local randomization

Description

rdsensitivity analyze the sensitivity of randomization p-values and confidence intervals to different window lengths.

Usage

rdsensitivity(
  Y,
  R,
  cutoff = 0,
  wlist,
  wlist_left,
  tlist,
  statistic = "diffmeans",
  p = 0,
  evalat = "cutoff",
  kernel = "uniform",
  fuzzy = NULL,
  ci = NULL,
  ci_alpha = 0.05,
  reps = 1000,
  seed = 666,
  nodraw = FALSE,
  quietly = FALSE
)
rdsensitivity(
  Y,
  R,
  cutoff = 0,
  wlist,
  wlist_left,
  tlist,
  statistic = "diffmeans",
  p = 0,
  evalat = "cutoff",
  kernel = "uniform",
  fuzzy = NULL,
  ci = NULL,
  ci_alpha = 0.05,
  reps = 1000,
  seed = 666,
  nodraw = FALSE,
  quietly = FALSE
)

Arguments

`Y`	a vector containing the values of the outcome variable.
`R`	a vector containing the values of the running variable.
`cutoff`	the RD cutoff (default is 0).
`wlist`	the list of windows to the right of the cutoff. By default the program constructs 10 windows around the cutoffwith 5 observations each.
`wlist_left`	the list of windows to the left of the cutoff. If not specified, the windows are constructed symmetrically around the cutoff based on the values in wlist.
`tlist`	the list of values of the treatment effect under the null to be evaluated. By default the program employs ten evenly spaced points within the asymptotic confidence interval for a constant treatment effect in the smallest window to be used.
`statistic`	the statistic to be used in the balance tests. Allowed options are `diffmeans` (difference in means statistic), `ksmirnov` (Kolmogorov-Smirnov statistic) and `ranksum` (Wilcoxon-Mann-Whitney standardized statistic). Default option is `diffmeans`. The statistic `ttest` is equivalent to `diffmeans` and included for backward compatibility.
`p`	the order of the polynomial for outcome adjustment model. Default is 0.
`evalat`	specifies the point at which the adjusted variable is evaluated. Allowed options are `cutoff` and `means`. Default is `cutoff`.
`kernel`	specifies the type of kernel to use as weighting scheme. Allowed kernel types are `uniform` (uniform kernel), `triangular` (triangular kernel) and `epan` (Epanechnikov kernel). Default is `uniform`.
`fuzzy`	indicates that the RD design is fuzzy. `fuzzy` can be specified as a vector containing the values of the endogenous treatment variable, or as a list where the first element is the vector of endogenous treatment values and the second element is a string containing the name of the statistic to be used. Allowed statistics are `ar` (Anderson-Rubin statistic) and `tsls` (2SLS statistic). Default statistic is `ar`. The `tsls` statistic relies on large-sample approximation.
`ci`	returns the confidence interval corresponding to the indicated window length. `ci` has to be a two-dimensional vector indicating the left and right limits of the window. Default alpha is .05 (95% level CI).
`ci_alpha`	Specifies value of alpha for the confidence interval. Default alpha is .05 (95% level CI).
`reps`	number of replications. Default is 1000.
`seed`	the seed to be used for the randomization tests.
`nodraw`	suppresses contour plot.
`quietly`	suppresses the output table.

Value

`tlist`	treatment effects grid
`wlist`	window grid
`results`	table with corresponding p-values for each window and treatment effect pair.
`ci`	confidence interval (if `ci` is specified).

Author(s)

Matias Cattaneo, Princeton University. [email protected]

Rocio Titiunik, Princeton University. [email protected]

Gonzalo Vazquez-Bare, UC Santa Barbara. [email protected]

References

Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2016). Inference in Regression Discontinuity Designs under Local Randomization. Stata Journal 16(2): 331-367.

Examples

# Toy dataset
R <- runif(100,-1,1)
Y <- 1 + R -.5*R^2 + .3*R^3 + (R>=0) + rnorm(100)
# Sensitivity analysis
# Note: low number of replications to speed up process.
# The user should increase the number of replications.
tmp <- rdsensitivity(Y,R,wlist=seq(.75,2,by=.25),tlist=seq(0,5,by=1),reps=500)


# Toy dataset
R <- runif(100,-1,1)
Y <- 1 + R -.5*R^2 + .3*R^3 + (R>=0) + rnorm(100)
# Sensitivity analysis
# Note: low number of replications to speed up process.
# The user should increase the number of replications.
tmp <- rdsensitivity(Y,R,wlist=seq(.75,2,by=.25),tlist=seq(0,5,by=1),reps=500)

Window selection for RD designs under local randomization

Description

rdwinselect implements the window-selection procedure based on balance tests for RD designs under local randomization. Specifically, it constructs a sequence of nested windows around the RD cutoff and reports binomial tests for the running variable runvar and covariate balance tests for covariates covariates (if specified). The recommended window is the largest window around the cutoff such that the minimum p-value of the balance test is larger than a prespecified level for all nested (smaller) windows. By default, the p-values are calculated using randomization inference methods.

Usage

rdwinselect(
  R,
  X,
  cutoff = 0,
  obsmin = NULL,
  wmin = NULL,
  wobs = NULL,
  wstep = NULL,
  wasymmetric = FALSE,
  wmasspoints = FALSE,
  dropmissing = FALSE,
  nwindows = 10,
  statistic = "diffmeans",
  p = 0,
  evalat = "cutoff",
  kernel = "uniform",
  approx = FALSE,
  level = 0.15,
  reps = 1000,
  seed = 666,
  plot = FALSE,
  quietly = FALSE,
  obsstep = NULL
)
rdwinselect(
  R,
  X,
  cutoff = 0,
  obsmin = NULL,
  wmin = NULL,
  wobs = NULL,
  wstep = NULL,
  wasymmetric = FALSE,
  wmasspoints = FALSE,
  dropmissing = FALSE,
  nwindows = 10,
  statistic = "diffmeans",
  p = 0,
  evalat = "cutoff",
  kernel = "uniform",
  approx = FALSE,
  level = 0.15,
  reps = 1000,
  seed = 666,
  plot = FALSE,
  quietly = FALSE,
  obsstep = NULL
)

Arguments

`R`	a vector containing the values of the running variable.
`X`	the matrix of covariates to be used in the balancing tests. The matrix is optional but the recommended window is only provided when at least one covariate is specified. This should be a matrix of size n x k where n is the total sample size and $k$ is the number of covariates.
`cutoff`	the RD cutoff (default is 0).
`obsmin`	the minimum number of observations above and below the cutoff in the smallest window. Default is 10.
`wmin`	the smallest window to be used.
`wobs`	the number of observations to be added at each side of the cutoff at each step. Default is 5.
`wstep`	the increment in window length.
`wasymmetric`	allows for asymmetric windows around the cutoff when (`wobs` is specified).
`wmasspoints`	specifies that the running variable is discrete and each masspoint should be used as a window.
`dropmissing`	drop rows with missing values in covariates when calculating windows.
`nwindows`	the number of windows to be used. Default is 10.
`statistic`	the statistic to be used in the balance tests. Allowed options are `diffmeans` (difference in means statistic), `ksmirnov` (Kolmogorov-Smirnov statistic), `ranksum` (Wilcoxon-Mann-Whitney standardized statistic) and `hotelling` (Hotelling's T-squared statistic). Default option is `diffmeans`. The statistic `ttest` is equivalent to `diffmeans` and included for backward compatibility.
`p`	the order of the polynomial for outcome adjustment model (for covariates). Default is 0.
`evalat`	specifies the point at which the adjusted variable is evaluated. Allowed options are `cutoff` and `means`. Default is `cutoff`.
`kernel`	specifies the type of kernel to use as weighting scheme. Allowed kernel types are `uniform` (uniform kernel), `triangular` (triangular kernel) and `epan` (Epanechnikov kernel). Default is `uniform`.
`approx`	forces the command to conduct the covariate balance tests using a large-sample approximation instead of finite-sample exact randomization inference methods.
`level`	the minimum accepted value of the p-value from the covariate balance tests. Default is .15.
`reps`	number of replications. Default is 1000.
`seed`	the seed to be used for the randomization tests.
`plot`	draws a scatter plot of the minimum p-value from the covariate balance test against window length.
`quietly`	suppress output
`obsstep`	the minimum number of observations to be added on each side of the cutoff for the sequence of fixed-increment nested windows. This option is deprecated and only included for backward compatibility.

Value

`window`	recommended window (NA is covariates are not specified)
`wlist`	list of window lengths
`results`	table including window lengths, minimum p-value in each window, corresponding number of the variable with minimum p-value (i.e. column of covariate matrix), Binomial test p-value and sample sizes to the left and right of the cutoff in each window.
`summary`	summary statistics.

Author(s)

Matias Cattaneo, Princeton University. [email protected]

Rocio Titiunik, Princeton University. [email protected]

Gonzalo Vazquez-Bare, UC Santa Barbara. [email protected]

References

Cattaneo, M.D., R. Titiunik and G. Vazquez-Bare. (2016). Inference in Regression Discontinuity Designs under Local Randomization. Stata Journal 16(2): 331-367.

Examples

# Toy dataset
X <- array(rnorm(200),dim=c(100,2))
R <- X[1,] + X[2,] + rnorm(100)
# Window selection adding 5 observations at each step
# Note: low number of replications to speed up process.
tmp <- rdwinselect(R,X,obsmin=10,wobs=5,reps=500)
# Window selection setting initial window and step
# The user should increase the number of replications.
tmp <- rdwinselect(R,X,wmin=.5,wstep=.125,reps=500)
# Window selection with approximate (large sample) inference and p-value plot
tmp <- rdwinselect(R,X,wmin=.5,wstep=.125,approx=TRUE,nwin=80,quietly=TRUE,plot=TRUE)


# Toy dataset
X <- array(rnorm(200),dim=c(100,2))
R <- X[1,] + X[2,] + rnorm(100)
# Window selection adding 5 observations at each step
# Note: low number of replications to speed up process.
tmp <- rdwinselect(R,X,obsmin=10,wobs=5,reps=500)
# Window selection setting initial window and step
# The user should increase the number of replications.
tmp <- rdwinselect(R,X,wmin=.5,wstep=.125,reps=500)
# Window selection with approximate (large sample) inference and p-value plot
tmp <- rdwinselect(R,X,wmin=.5,wstep=.125,approx=TRUE,nwin=80,quietly=TRUE,plot=TRUE)

Package 'rdlocrand'

Help Index

rdlocrand: Local Randomization Methods for RD Designs

Description

Author(s)

References

Randomization Inference for RD Designs under Local Randomization

Description

Usage

Arguments

Value

Author(s)

References

Examples

Rosenbaum bounds for RD designs under local randomization

Description

Usage

Arguments

Value

Author(s)

References

Examples

Sensitivity analysis for RD designs under local randomization

Description

Usage

Arguments

Value

Author(s)

References

Examples

Window selection for RD designs under local randomization

Description

Usage

Arguments

Value

Author(s)

References

Examples