plot_breakpoints: Calculating and plotting breakpoints in time series

In swash: Swash-Backwash Model for the Single Epidemic Wave

Calculating and plotting breakpoints in time series

Description

The function calculates breakpoints for time series and provides plots of the analysis results

Usage

plot_breakpoints(
  dataset, 
    formula,
    alpha = 0.05,
    line.col = "chocolate",
    ci.col = c(
      rgb(0, 0, 255, maxColorValue = 255, alpha = 0.5),
      rgb(0, 255, 0, maxColorValue = 255, alpha = 0.5),
      rgb(255, 0, 0, maxColorValue = 255, alpha = 0.5)
    ),
    legend.show = TRUE, 
    legend.pos = c(
      "topright", 
      "topright", 
      "topright", 
      "topright"
      ),
    xlab = "Time", 
    ylab = "Y", 
    ylim = NULL,
    plot.main = c(
      "Breakpoints", 
      "Segment model fits", 
      "BIC and Residual Sum of Squares", 
      "Model without breaks (one segment)"
    ),
    output.full = TRUE,
    separate_plots = FALSE,
    ...
  )

Arguments

dataset	data.frame object with (infections) time series
formula	formula object for linear regression formula
alpha	Significance level \alpha for 1-\alpha*100 confidence intervals
line.col	Line color in time series plot
ci.col	Colors for confidence intervals in 3 plots
legend.show	bool: Show legend?
legend.pos	Position of legend
xlab	Label of x axis
ylab	Label of y axis
ylim	Limits of y axis
plot.main	Titles for 4 plots
output.full	bool: All 4 plots or just one (breakpoints)?
separate_plots	bool: All 4 plots in one or separate?
...	Further arguments passed to breakpoints()

Details

The function uses the breakpoints() function from the strucchange package (Zeileis et al. 2003) to estimate breaks in time series given by the user. These breakpoints are visualized, confidence intervals and model diagnostics are calculated, and for M breakpoints, M+1 models are estimated and returned.

Value

data.frame with one row for each model segment with coefficients, R^2, etc.

Author(s)

Thomas Wieland

References

Wieland T (2020) A phenomenological approach to assessing the effectiveness of COVID-19 related nonpharmaceutical interventions in Germany. Safety Science 131, 104924. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1016/j.ssci.2020.104924")}

Zeileis A, Kleiber C, Krämer W, Hornik K (2003) Testing and dating of structural changes in practice. Computational Statistics & Data Analysis 44(1-2) 109–123. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1016/S0167-9473(03)00030-6")}

See Also

logistic_growth, exponential_growth

Examples

data(Infections)
# Confirmed SARS-CoV-2 cases in Germany

plot_breakpoints(
  Infections, 
  log(infections_daily) ~ day,
  output.full = TRUE
  )
# Breakpoints for time series

swash documentation built on Feb. 15, 2026, 5:07 p.m.