Determine Type of Regression Discontinuity Design

rd_type cross-tabulates observations based on (1) a binary treatment and (2) one or two assignments and their cutoff values. This is an internal function and is typically not directly invoked by the user. It can be accessed using the triple colon, as in rddapp:::rd_type().

Usage

rd_type(
  data,
  treat,
  assign_1,
  cutoff_1,
  operator_1 = NULL,
  assign_2 = NULL,
  cutoff_2 = NULL,
  operator_2 = NULL
)

Arguments

data

A data.frame, with each row representing an observation.

treat

A string specifying the name of the numeric treatment variable (treated = positive values).

assign_1

A string specifying the variable name of the primary assignment.

cutoff_1

A numeric value containing the cutpoint at which assignment to the treatment is determined, for the primary assignment.

operator_1

The operator specifying the treatment option according to design for the primary assignment. Options are "g" (treatment is assigned if x1 is greater than its cutoff), "geq" (treatment is assigned if x1 is greater than or equal to its cutoff), "l" (treatment is assigned if x1 is less than its cutoff), and "leq" (treatment is assigned if x1 is less than or equal to its cutoff).

assign_2

An optional string specifying the variable name of the secondary assignment.

cutoff_2

An optional numeric value containing the cutpoint at which assignment to the treatment is determined, for the secondary assignment.

operator_2

The operator specifying the treatment option according to design for the secondary assignment. Options are "g" (treatment is assigned if x2 is greater than its cutoff), "geq" (treatment is assigned if x2 is greater than or equal to its cutoff), "l" (treatment is assigned if x2 is less than its cutoff), and "leq" (treatment is assigned if x2 is less than or equal to its cutoff).

Value

rd_type returns a list of two elements:

crosstab

The cross-table as a data.frame. Columns in the dataframe include treatment rules, number of observations in the control condition, number of observations in the treatment condition, and the probability of an observation being in treatment or control.

type

A string specifying the type of design used, either "SHARP" or "FUZZY".

Examples

set.seed(12345)
x <- runif(1000, -1, 1)
cov <- rnorm(1000)
y <- 3 + 2 * x + 3 * cov + 10 * (x >= 0) + rnorm(1000)
df <- data.frame(cbind(y, x, t = x>=0))
rddapp:::rd_type(df, 't', 'x', 0, 'geq')
#> $crosstab
#>    A1 Control Treat Prob.
#> 1 < 0     469     0     0
#> 2 ≥ 0       0   531     1
#> 
#> $type
#> [1] "SHARP"
#>