ordinary_events: Identifies ordinary rainfall events by calculating the...

In mevr: Fitting the Metastatistical Extreme Value Distribution MEVD

Identifies ordinary rainfall events by calculating the maximum within rainfall events defined with event_separation

Description

This function calculates the rolling sum of values in the provided data over a specified duration and identifies the events with the highest sum within the defined time intervals. It is useful for detecting significant events based on aggregated values over time.

Usage

ordinary_events(x, duration, na.rm = TRUE)

Arguments

x	A list containing at least the following elements (usually the output of function event_separation): 'data': A data frame or data table with at least two columns: 'val' (the values to sum) and 'groupvar' (timestamps or another grouping variable). 'ts_res': The time resolution of the data in minutes (i.e., how many minutes each time step represents). 'fromto': A data frame with two columns: 'from' and 'to', representing the start and end indices of the time intervals to analyze.
duration	Numeric. The duration in minutes for which maxima shall be calculated.
na.rm	Logical. If na.rm = TRUE (default), missing values within an event are ignored in the rolling sum. If na.rm = FALSE, windows that contain missing values are not considered for the event maximum.

Value

Returns a tibble with individual rainfall events that can be used as input for functions fsmev, fmev, ftmev.

Examples

## Not run: 
# Example usage
x <- list(
  data = data.frame(val = runif(100),
  groupvar = seq.POSIXt(Sys.time(),
                        by = "10 min",
                        length.out = 100)
                        ),
  ts_res = 10,
  fromto = data.frame(from = c(1, 51), to = c(50, 100))
)
duration <- 30
result <- ordinary_events(x, duration)
print(result)

## End(Not run)

mevr documentation built on Oct. 13, 2026, 5:07 p.m.