randinf: Randomization Inference

In axlehner/SpatialRDD: Conduct Multiple Types of Geographic Regression Discontinuity Designs

Randomization Inference

Description

Text

Usage

randinf(
  depvar,
  points.sf,
  lambdapoisl,
  nruns,
  id = "id",
  geometry = TRUE,
  sentinel_tries = 10,
  ...
)

Arguments

depvar	the dependent variable as a string
points.sf	sf data.frame containing all points and the relevant variables
lambdapoisl	the parameter for the poisson line process (see rpoisline from spatstat for details)
nruns	number of randomization runs (ideally in the thousands, start by trying a few dozen to check performance and speed)
id	the unique id column in the points frame
geometry	should the return results frame contain geometries so that all placebo/randomization lines can be plotted on a map?
sentinel_tries	sometimes the poisson process creates lines that do not cross any points, thus leading to no control units (expected to happen by chance). This parameter determines how often the function should retry in case this happnes (default is 10).
...	pass arguments to rdrobust

Value

a randomization inference p-value or, alternatively, a data frame containing all simulated lines with the respective estimates

References

Baddeley A, Turner R (2005). “spatstat: An R Package for Analyzing Spatial Point Patterns.” Journal of Statistical Software, 12(6), 1–42. doi:10.18637/jss.v012.i06. Baddeley A, Rubak E, Turner R (2015). Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press, London. ISBN 9781482210200

Examples

points_samp.sf <- sf::st_sample(polygon_full, 1000) # create points
# make it an sf object bc st_sample only created the geometry list-column (sfc):
points_samp.sf <- sf::st_sf(points_samp.sf)
# add a unique ID to each observation:
points_samp.sf$id <- 1:nrow(points_samp.sf)
# assign treatment:
points_samp.sf$treated <- assign_treated(points_samp.sf, polygon_treated, id = "id")
# first we define a variable for the number of "treated" and control
NTr <- length(points_samp.sf$id[points_samp.sf$treated == 1])
NCo <- length(points_samp.sf$id[points_samp.sf$treated == 0])
# the treated areas get a 10 percentage point higher literacy rate
points_samp.sf$education[points_samp.sf$treated == 1] <- 0.7
points_samp.sf$education[points_samp.sf$treated == 0] <- 0.6
# and we add some noise, otherwise we would obtain regression coeffictions with no standard errors
points_samp.sf$education[points_samp.sf$treated == 1] <- rnorm(NTr, mean = 0, sd = .1) +
  points_samp.sf$education[points_samp.sf$treated == 1]
points_samp.sf$education[points_samp.sf$treated == 0] <- rnorm(NCo, mean = 0, sd = .1) +
  points_samp.sf$education[points_samp.sf$treated == 0]

results.ri <- randinf("education", points_samp.sf, 0.0001, 100)

axlehner/SpatialRDD documentation built on March 24, 2024, 2:03 p.m.