EWSmethods (version 1.3.1)

ewsnet_predict: EWSNet Predict

Description

Communicates with EWSNet (https://ewsnet.github.io), a deep learning framework for modelling and anticipating regime shifts in dynamical systems, and returns the model's prediction for the inputted univariate time series.

Usage

ewsnet_predict(
  x,
  scaling = TRUE,
  ensemble = 25,
  envname,
  weights_path = default_weights_path()
)

Value

A dataframe of EWSNet predictions. Values represent the estimated probability that the quoted event will occur.

Arguments

x

A numeric vector of values to be tested.

scaling

Boolean. If TRUE, the time series will be scaled between 1 and 2 and scaled EWSNet model weights will be used. This is the recommended setting.

ensemble

A numeric value stating the number of models to average over. Options range from 1 to 25.

envname

A string naming the Python environment prepared by ewsnet_init().

weights_path

A string naming the path to model weights installed by ewsnet_reset().

Examples

#A dummy dataset of a hedgerow bird population
#monitored over 50 years.

abundance_data <- data.frame(time = seq(1:50),
 abundance = rnorm(50,mean = 20))

#Activate python environment (only necessary
#on first opening of R session).

if (FALSE) {
ewsnet_init(envname = "EWSNET_env")
}

#Generate EWSNet predictions.

if (FALSE) {
pred <- ewsnet_predict(
 abundance_data$abundance,
 scaling = TRUE,
 ensemble = 15,
 envname = "EWSNET_env")
 }