plot_data: Extract the tidy data behind a coresynth plot

In coresynth: Fast and Unified Synthetic Control Methods

Extract the tidy data behind a coresynth plot

Description

Returns the tidy data.frame that plot() draws for a given type, so the underlying series, weights, or placebo paths can be inspected, joined into a table, or re-plotted directly. plot() stays the quick path; plot_data() is the handle for anyone who wants to relabel simplified series names, feed the numbers into their own figure, or postprocess them further.

Usage

plot_data(x, ...)

## Default S3 method:
plot_data(x, ...)

## S3 method for class 'coresynth'
plot_data(
  x,
  type = c("trend", "gap", "weights", "pred_weights"),
  align = FALSE,
  top_n = Inf,
  show_donors = 0,
  ...
)

## S3 method for class 'scm_placebo'
plot_data(x, type = c("gaps", "ratios"), mspe_prune = Inf, ...)

Arguments

x	A coresynth fit (from scm_fit()) or a scm_placebo object (from mspe_ratio_pval()).
...	Passed to methods (unused by the current methods).
type	For a coresynth fit: one of "trend", "gap", "weights", or "pred_weights", matching plot.coresynth(). For a scm_placebo object: "gaps" or "ratios", matching plot.scm_placebo().
align	For type = "trend"/"gap": when TRUE, shift the synthetic series by its pre-treatment level gap to the treated series, exactly as in plot.coresynth(). Default FALSE (raw series).
top_n	For type = "weights"/"pred_weights": keep only the top_n largest weights (default Inf, every row).
show_donors	For type = "trend": also return the outcome paths of the show_donors largest-weight donors as series = "Donors" rows, adding a unit column that identifies each donor (NA for the treated and synthetic series). Default 0 (treated and synthetic series only).
mspe_prune	For a scm_placebo object with type = "gaps": drop placebo units whose pre-treatment MSPE exceeds mspe_prune times the treated unit's, as in plot.scm_placebo(). Default Inf (no pruning).

Details

The frame mirrors what the matching plot(x, type = ...) call shows, with two deliberate departures that make it a better data source:

• Plain column names (time, value, series, weight, ...) are used instead of the dotted convention of augment(), since this is data to manipulate rather than model-augmented observations.

• The cosmetic "drop donors with weight below 1e-4" filter that plot(type = "weights") applies is not used here: every donor is returned (use top_n to subset), so the frame is the complete set of weights.

Only the arguments that change which rows or values appear are accepted (align, top_n, show_donors, mspe_prune); purely cosmetic arguments of plot() (colors, labels, linetypes, vline, fill, ...) have no data counterpart and are not part of this interface.

Value

A tidy data.frame. Columns by type:

"trend"

time, value, series ("Treated" / "Synthetic Control"); with show_donors > 0, also "Donors" rows and a unit column.

"gap"

time, gap (treated minus synthetic control).

"weights"

unit, weight; SDID fits add a panel column ("omega" unit weights, "lambda" time weights), with unit holding the pre-period label for "lambda" rows.

"pred_weights"

predictor, weight (sharp SCM only).

"gaps"

time, gap, unit (NA for the treated series), series ("Treated" / "Placebo (donor pool)").

"ratios"

unit, ratio, series.

See Also

plot.coresynth(), plot.scm_placebo()

Examples

set.seed(1)
panel <- expand.grid(unit = 1:10, year = 1:20)
panel$treated <- as.integer(panel$unit == 5 & panel$year > 15)
panel$gdp <- panel$unit + 0.5 * panel$year +
  rnorm(nrow(panel)) + 3 * panel$treated
fit <- scm_fit(gdp ~ treated | unit + year, data = panel, method = "scm")

head(plot_data(fit, type = "trend"))
plot_data(fit, type = "gap")
plot_data(fit, type = "weights")

# Relabel the simplified series names, then plot it yourself
df <- plot_data(fit, type = "trend")
df$series <- sub("Synthetic Control", "Synthetic Unit 5", df$series)

ggplot2::ggplot(df, ggplot2::aes(time, value, color = series)) +
  ggplot2::geom_line()

coresynth documentation built on Aug. 28, 2026, 1:06 a.m.