Package {fixes}

Autoplot for event-study results

Description

S3 method that plots an es_result (from run_es()). It forwards arguments to plot_es.

Usage

## S3 method for class 'es_result'
autoplot(object, ci_level = 0.95, type = "ribbon", ...)

Arguments

Value

A ggplot2 ggplot object.

Examples

# res <- run_es(...)
# ggplot2::autoplot(res, ci_level = 0.95, type = "ribbon")

Plot the ATT(g,t) matrix from a Callaway-Sant'Anna event study

Description

Visualises the full cohort-by-period ATT(g,t) matrix stored in the att_gt attribute of an es_result object produced by run_es(estimator = "cs"). Two display styles are available:

• "heatmap": a tile plot with calendar time t on the x-axis and cohort g on the y-axis, colour-filled by the point estimate. Cells whose pointwise confidence interval excludes zero are marked with a filled dot; cells that are simultaneously significant (when bootstrap data are available) are additionally marked with an open diamond.

• "facet": one panel per cohort showing ATT(g,t) over calendar time t with a pointwise confidence ribbon, mirroring the style of plot_es. A lighter simultaneous CI ribbon is overlaid when bootstrap data are available.

Both types draw a vertical dashed line at t = g (treatment onset) for each cohort.

Usage

plot_att_gt(
  x,
  type = c("heatmap", "facet"),
  ci_level = 0.95,
  zero_line = TRUE,
  theme = c("bw", "minimal", "classic"),
  color = "#B25D91FF",
  fill = "#B25D91FF",
  alpha = 0.2
)

## S3 method for class 'att_gt_result'
autoplot(object, ...)

Arguments

Value

A ggplot object.

Bootstrap annotation

When attr(x, "bootstrap") is present (i.e., run_es() was called with bootstrap = TRUE), both plot types add simultaneous inference overlays sourced from the (g,t)-level bootstrap object.

See Also

plot_es, run_es

Examples

## Not run: 
cs_result <- run_es(data = mydata, outcome = y, time = year,
                    timing = g, unit = id, fe = ~id + year,
                    staggered = TRUE, estimator = "cs")
plot_att_gt(cs_result)
plot_att_gt(cs_result, type = "facet")

## End(Not run)

Plot event-study results with ribbons or error bars

Description

Plot event-study results with ribbons or error bars

Usage

plot_es(
  data,
  ci_level = 0.95,
  type = "ribbon",
  vline_val = 0,
  vline_color = "#000",
  hline_val = 0,
  hline_color = "#000",
  linewidth = 1,
  pointsize = 2,
  alpha = 0.2,
  barwidth = 0.2,
  color = "#B25D91FF",
  fill = "#B25D91FF",
  theme_style = "bw",
  show_simultaneous = FALSE
)

Arguments

Value

A ggplot object.

Examples

# Assuming `res <- run_es(...)`
# p <- plot_es(res, ci_level = 0.95, type = "ribbon")
# print(p)

Interactive event-study plot with hover details

Description

Creates an interactive plotly visualization of event study results with hover-over displays showing coefficients, confidence intervals, and other details.

Usage

plot_es_interactive(
  data,
  ci_level = 0.95,
  vline_val = 0,
  hline_val = 0,
  vline_color = "#000",
  hline_color = "#000",
  color = "#B25D91FF",
  fill = "#B25D91FF",
  alpha = 0.2,
  linewidth = 2,
  markersize = 8,
  show_ribbon = TRUE,
  show_simultaneous = FALSE,
  height = NULL,
  width = NULL
)

Arguments

Details

The hover tooltip displays:

• Relative time to treatment

• Point estimate (coefficient)

• Confidence interval bounds

• Standard error

• P-value

• Simultaneous CI bounds (when show_simultaneous = TRUE)

Value

A plotly object that can be displayed interactively.

Examples

## Not run: 
# Assuming res <- run_es(...)
plot_es_interactive(res)
plot_es_interactive(res, ci_level = 0.99, show_ribbon = FALSE)
plot_es_interactive(res, show_simultaneous = TRUE)

## End(Not run)

Event Study Estimation for Panel Data

Description

Runs an event study regression on panel data, supporting both classic (universal timing) and staggered (unit-varying timing via sunab). The function builds the design (lead/lag factor or sunab), estimates with fixest, and returns a tidy table with metadata.

Usage

run_es(
  data,
  outcome,
  treatment = NULL,
  time,
  timing,
  fe = NULL,
  lead_range = NULL,
  lag_range = NULL,
  covariates = NULL,
  cluster = NULL,
  weights = NULL,
  baseline = -1L,
  interval = 1,
  time_transform = FALSE,
  unit = NULL,
  staggered = FALSE,
  method = c("classic", "sunab"),
  estimator = c("twfe", "cs", "sa", "bjs"),
  control_group = c("nevertreated", "notyettreated"),
  anticipation = 0L,
  conf.level = 0.95,
  vcov = "HC1",
  vcov_args = list(),
  bootstrap = FALSE,
  B = 999L,
  alpha = 0.05,
  boot_seed = NULL
)

Arguments

Value

A data.frame of class "es_result" with columns:

• term, estimate, std.error, statistic, p.value

• conf_low_XX, conf_high_XX (for each requested conf.level)

• relative_time (integer; 0 = event), is_baseline (logical; marks the reference period)

Attributes include: lead_range, lag_range, baseline, interval, call, model_formula, conf.level, N, N_units, N_treated, N_nevertreated, fe, vcov_type, cluster_vars, staggered, sunab_used.

Key Features

• One-step event study: specify outcome, treatment, time, timing, fixed effects, and (optionally) covariates.

• Switch between Classic (factor expansion) and Staggered-SAFE (method = "sunab").

• Flexible clustering, weights, and VCOV choices (e.g., vcov = "HC1" | "HC3" | "CR2" | "iid" ...).

• Automatic lead/lag window detection and customizable baseline period.

• Returns an "es_result" object compatible with print() and autoplot().