SCtools (version 0.3.3.1)

plot_placebos: Function to plot placebos of a synthetic control analysis

Description

Creates plots with the difference between observed units and synthetic controls for the treated and control units. See Abadie, Diamond, and Hainmueller (2011).

Usage

plot_placebos(
  tdf = tdf,
  discard.extreme = FALSE,
  mspe.limit = 20,
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  alpha.placebos = 1,
  ...
)

Value

p.gaps Gaps plot indicating difference between the treated unit, the placebos, and their respective synthetic controls.

Arguments

tdf

An object with a list of outcome values for placebos, constructed by generate.placebos.

discard.extreme

Logical. Whether or not units with high pre-treatement MSPE should be excluded from the plot. Takes a default of FALSE.

mspe.limit

Numerical. Used if discard.extreme is TRUE. It indicates how many times the pre-treatment MSPE of a placebo should be higher than that of the treated unit to be considered extreme and discarded. Default is 20.

xlab

Character. Optional. Label of the x axis.

ylab

Character. Optional. Label of the y axis.

title

Character. Optional. Title of the plot.

alpha.placebos

the transparency setting, default of 1

...

optional arguments (currently not used)

See Also

generate.placebos, gaps.plot, synth, dataprep

Examples

## Example with toy data from Synth
library(Synth)
# Load the simulated data
data(synth.data)

# Execute dataprep to produce the necessary matrices for synth
dataprep.out<-
  dataprep(
    foo = synth.data,
    predictors = c("X1"),
    predictors.op = "mean",
    dependent = "Y",
    unit.variable = "unit.num",
    time.variable = "year",
    special.predictors = list(
      list("Y", 1991, "mean")
    ),
    treatment.identifier = 7,
    controls.identifier = c(29, 2, 17),
    time.predictors.prior = c(1984:1989),
    time.optimize.ssr = c(1984:1990),
    unit.names.variable = "name",
    time.plot = 1984:1996
)

# run the synth command to create the synthetic control
synth.out <- synth(dataprep.out, Sigf.ipop=1)

tdf <- generate.placebos(dataprep.out,synth.out, Sigf.ipop = 1)
## Plot the gaps in outcome values over time of each unit --
## treated and placebos -- to their synthetic controls

p <- plot_placebos(tdf,discard.extreme=TRUE, mspe.limit=10, xlab='Year')
p

## Example with toy data from Synth
library(Synth)
# Load the simulated data
data(synth.data)

# Execute dataprep to produce the necessary matrices for synth
dataprep.out<-
  dataprep(
    foo = synth.data,
    predictors = c("X1"),
    predictors.op = "mean",
    dependent = "Y",
    unit.variable = "unit.num",
    time.variable = "year",
    special.predictors = list(
      list("Y", 1991, "mean")
    ),
    treatment.identifier = 7,
    controls.identifier = c(29, 2, 13, 17),
    time.predictors.prior = c(1984:1989),
    time.optimize.ssr = c(1984:1990),
    unit.names.variable = "name",
    time.plot = 1984:1996
)

# run the synth command to create the synthetic control
synth.out <- synth(dataprep.out, Sigf.ipop=2)

## run the generate.placebos command to reassign treatment status
## to each unit listed as control, one at a time, and generate their
## synthetic versions. Sigf.ipop = 2 for faster computing time. 
## Increase to the default of 5 for better estimates. 
tdf <- generate.placebos(dataprep.out,synth.out, Sigf.ipop = 2, strategy='multicore')

## Plot the gaps in outcome values over time of each unit --
## treated and placebos -- to their synthetic controls

p <- plot_placebos(tdf,discard.extreme=TRUE, mspe.limit=10, xlab='Year')
p