Plotting

Andreas M. Brandmaier

2023-05-10

library(pdcsync)

N <- 1000

t1 <- arima.sim(model = list(ar = 0.2), n = N)
t2 <- arima.sim(model = list(ar = 0.2), n = N)

t2[500:700] <- t1[500:700+10]

t1[1:300] <- t2[1:300+3]

result <- pdcsync(t1, t2, segment_width = 100, search_width = 5)

Plot the simple synchronisation profile

plot(result)

## Warning: Transformation introduced infinite values in discrete y-axis

## Warning: Removed 282 rows containing missing values (`geom_line()`).

Suppress the best-fitting synchronisation line:

plot(result, show_sync_line = FALSE)

Show the line again and vary output:

plot(result, show_sync_line = TRUE, sync_lwd=1, sync_col="green")

Plot the extended synchronisation profile including the time series.

syncplot(result)

## Don't know how to automatically pick scale for object of type <ts>. Defaulting
## to continuous.
## Don't know how to automatically pick scale for object of type <ts>. Defaulting
## to continuous.

Annotate segments that are “leading” segments with green areas:

syncplot(result, annotate_segments = TRUE)

## Don't know how to automatically pick scale for object of type <ts>. Defaulting
## to continuous.
## Don't know how to automatically pick scale for object of type <ts>. Defaulting
## to continuous.