uniEWS {EWSmethods}

Univariate Early Warning Signal Assessment

Description

A function for performing early warning signal (EWS) assessment on univariate time series. Both rolling and expanding window methods of EWS assessment can be performed with the assessments returned as a dataframe.

Usage

uniEWS(
  data,
  metrics,
  method = c("expanding", "rolling"),
  winsize = 50,
  burn_in = 5,
  threshold = 2,
  tail.direction = "one.tailed",
  trait = NULL
)

Arguments

data

A dataframe where the first column is an equally spaced time vector and the second column is the time series to be assessed.

metrics

String vector of early warning signal metrics to be assessed. Options include: "ar1", "cv", "SD", "acf", "rr", "dr", "skew", "kurt", and "trait" (only available if method = "expanding").

method

Single string of either "expanding" or "rolling". "expanding" calls composite, expanding window EWS assessment. "rolling" calls typical, rolling window EWS assessment.

winsize

Numeric value. If method = "rolling", defines the window size of the rolling window as a percentage of the time series length.

burn_in

Numeric value. If method = "expanding", defines the number of data points to 'train' signals prior to EWS assessment.

threshold

Numeric value of either 1 or 2. Threshold*sigma is the value which, if the EWS strength exceeds it, constitutes a "signal".

tail.direction

String of either "one.tailed" or "two.tailed". "one.tailed" only indicates a warning if positive threshold sigma exceeded. "two.tailed" indicates a warning if positive OR negative threshold*sigma exceeded.

trait

A vector of numeric trait values if desired. Can be NULL

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. If method = "expanding", then each metric has been rbound into a single dataframe and extra columns are provided indicating whether the threshold*sigma value has been exceeded (i.e. "threshold.crossed"). If method = "rolling", then each metric's evolution over time is returned in individual columns.

EWS$cor

Dataframe of Kendall Tau correlations. Only returned if method = "rolling".

Examples

#A dummy dataset of a hedgerow bird population over
#25 years where both the number of individuals and
#the average bill length has been measured.

abundance_data <- data.frame(time = seq(1:25),
 abundance = rnorm(25,mean = 20),
 trait = rnorm(25,mean=1,sd=0.5))

#The early warning signal metrics to compute.

ews_metrics <- c("SD","ar1","skew")

#Rolling window early warning signal assessment of
#the bird abundance.

roll_ews <- uniEWS(
 data = abundance_data[,1:2],
 metrics =  ews_metrics,
 method = "rolling",
 winsize = 50)

#Expanding window early warning signal assessment of
#the bird abundance (with plotting).

exp_ews <- uniEWS(
 data = abundance_data[,1:2],
 metrics = ews_metrics,
 method = "expanding",
 burn_in = 10)

#Expanding window early warning signal assessment of
#the bird abundance incorporating the trait
#information.

ews_metrics_trait <- c("SD","ar1","trait")

trait_exp_ews <- uniEWS(
 data = abundance_data[,1:2],
 metrics = ews_metrics_trait,
 method = "expanding",
 burn_in = 10,
 trait = abundance_data$trait)