EWSmethods (version 1.3.1)

perm_rollEWS: Significance Testing of Rolling Window Early Warning Signals

Description

A function for identifying whether a warning has been generated from rolling early warning signal data using permutation tests. If a parallel connection is setup via parallel or future prior to usage of perm_rollEWS(), then the function is parallelised.

Usage

perm_rollEWS(
  data,
  metrics,
  winsize = 50,
  variate = c("uni", "multi"),
  perm.meth = "arima",
  iter = 500
)

Value

A list containing up to two objects: EWS outputs through time (EWS), and an identifier string (method).

EWS$raw

Dataframe of EWS measurements through time. Each metric's evolution over time is returned in individual columns.

EWS$cor

Dataframe of Kendall Tau correlations and permuted p-values.

EWS$dimred.ts

Dataframe containing the dimension reduction time series. Only returned if variate = "multi".

Arguments

data

A dataframe where the first column is an equally spaced time vector and the remainder column are the time series to be assessed. If a two column dataframe is provided, and variate = "uni", uniEWS() is called, whereas if number of columns exceeds two & variate = "multi", then multiEWS() is called.

metrics

String vector of early warning signal metrics to be assessed. For variate = "uni" these include: "ar1", "cv", "SD", "acf", "rr", "dr", "skew" and "kurt". For variate = "multi", pptions include: "meanSD", "maxSD", "meanAR", "maxAR", "eigenMAF", "mafAR", "mafSD", "pcaAR", "pcaSD", "eigenCOV", "maxCOV" and "mutINFO".

winsize

Numeric value. Defines the window size of the rolling window as a percentage of the time series length.

variate

String. Is a "uni"variate or "multi"variate assessment to be made.

perm.meth

String dictating the pseudo-randomisation technique to be used. Options include: "arima" (sampled from predictions of an ARIMA model), "red.noise" (red noise process using data mean, variance and autocorrelation coef) or "sample" (sampled from observed data without replacement).

iter

Numeric value. The number of permutations.

Examples

data(simTransComms)

#Permute p value for a univariate
#time series using resampling

#(data trimmed for speed)
perm_uni <- perm_rollEWS(
data = simTransComms$community1[1:10,2:3],
 winsize = 75,
 variate = "uni",
 metrics = c("ar1", "SD", "skew"),
 perm.meth = "sample",
 iter = 25)

 #Permute p value for a multivariate
#community using a red.noise process

# \donttest{
#if parallelisation desired,
#this can be achieved using the
#below code
cl <- parallel::makeCluster(2)
# }

# \donttest{
doParallel::registerDoParallel(cl)
# }

perm_multi <- perm_rollEWS(
data = simTransComms$community1[1:10,2:7],
 winsize = 75,
 variate = "multi",
 metrics = c("meanAR", "maxAR", "meanSD"),
 perm.meth = "red.noise",
 iter = 25)

# \donttest{
parallel::stopCluster(cl)
# }