nonstat (version 0.0.6)

is.nonstat: Test for nonstationarity

Description

Applies a nonvisual, diagnostic-based screening procedure to determine whether a univariate time series violates the assumption of stationarity. Specifically, the function evaluates (a) the presence of a trend and (b) changes in variance over time. These two dimensions of nonstationarity are assessed using two R-hat-type statistics adapted from Bayesian convergence diagnostics and Levene's test.

Usage

is.nonstat(tseries, nEp = 2, cut.psr1 = 1.1, cut.psr2 = 1.01, span = 3)

Value

a logical scalar indicating whether the prcoess has been diagnosed as non-stationary (TRUE) or stationary (FALSE)

Arguments

tseries

a numerical vector

nEp

number of epochs (in which time series is cut for PSR calculation)

cut.psr1

threshold for the trend diagnostic, Rhat(1), which assesses whether a process is trending

cut.psr2

threshold for the changing variance diagnostic, Rhat(2), which assesses whether the processe's variance is changing over time

span

numerical value that is passed to the loess function

References

Zitzmann, S., Lindner, C., Lohmann, J. F., & Hecht, M. (2024). "A Novel Nonvisual Procedure for Screening for Nonstationarity in Time Series as Obtained from Intensive Longitudinal Designs" Preprint

Examples

set.seed( 8332278 )
x <- rnorm( 50 )
is.nonstat( x )