ffaframework (version 0.1.1)

data_mw_variability: Estimate Variance for Annual Maximum Series Data

Description

Generates a time series of standard deviations using a moving window algorithm, which can be used to explore potential evidence of nonstationarity in the variability of a dataset. It returns a list that pairs each window’s mean year with its window standard deviation. The hyperparameters size and step control the behaviour of the moving window. Following the simulation findings from Vidrio-Sahagún and He (2022), the default window size and step are set to 10 and 5 years respectively. However, these can be changed by the user.

Usage

data_mw_variability(data, years, size = 10L, step = 5L)

Value

A list with two entries:

• years: Numeric vector containing the mean year within each window.

• std: Numeric vector of standard deviations within each window.

Arguments

data

Numeric vector of observed annual maximum series values. Must be strictly positive, finite, and not missing.

years

Numeric vector of observation years corresponding to data. Must be the same length as data and strictly increasing.

size

Integer scalar. The number of years in each moving window. Must be a positive number less than or equal to length(data) (default is 10).

step

Integer scalar. The offset (in years) between successive moving windows. Must be a positive number (default is 5).

References

Vidrio-Sahagún, C. T., and He, J. (2022). The decomposition-based nonstationary flood frequency analysis. Journal of Hydrology, 612 (September 2022), 128186. tools:::Rd_expr_doi("10.1016/j.jhydrol.2022.128186")

Examples

data <- rnorm(n = 100, mean = 100, sd = 10)
years <- seq(from = 1901, to = 2000)
data_mw_variability(data, years)