Detect excursions in synthetic datasets that mimic a real on

Detect excursions in synthetic datasets that mimic a real on

testNullHypothesis(
  time,
  vals,
  changeFun,
  n.ens = 100,
  mc.ens = 100,
  surrogate.method = "isospectral",
  seed = round(sum(vals, na.rm = FALSE)),
  progress = TRUE,
  ...
)

Arguments

time

a time vector, or matrix of time ensemble members (ensembles in columns)

vals

a values vector, or matrix of values ensemble members (ensembles in columns)

changeFun

the change function to across which to propagate

n.ens

How many ensembles to use for error propagation? (default = 100)

mc.ens

How many Monte Carlo simulations to use for null hypothesis testing

surrogate.method

What method to use to generage surrogate data for hypothesis testing? Options include:

  • 'isospectral': (Default) Following Ebisuzaki (1997), generate surrogates by scrambling the phases of the data while preserving their power spectrum. This uses the To generate these "isospectral" surrogates. Uses the rEDM::make_surrogate_data() or rEDM::SurrogateData() function depending on version

  • 'isopersistent': Generates surrogates by simulating from an autoregressive process of order 1 (AR(1)), which has been fit to the data. Uses the geoChronR::createSyntheticTimeseries() function

  • 'shuffle': Randomly shuffles the data to create surrogates. Uses the rEDM::make_surrogate_data() or rEDM::SurrogateData() function depending on version

seed

set a seed for reproducibility

progress

show null hypothesis testing progress bar?

...

Arguments passed on to propagateUncertainty

simulate.time.uncertainty

TRUE or FALSE. If an ensemble is not included, do you want to simulate time ensembles (default = TRUE)

simulate.paleo.uncertainty

TRUE or FALSE. If an ensemble is not included, do you want to simulate paleo ensembles (default = TRUE)

bam.model

BAM Model parameters to use if simulating time uncertainty (default = list(ns = n.ens, name = "bernoulli", param = 0.05), paleo.uncertainty = sd(vals,na.rm = TRUE)))

paleo.uncertainty

Uncertainty to use if modelling uncertainty for paleo values. (default = sd(vals,na.rm = TRUE)/2)

paleo.ar1

Autocorrelation coefficient to use for modelling uncertainty on paleoData, what fraction of the uncertainties are autocorrelated? (default = sqrt(0.5); or 50% autocorrelated uncertainty)

paleo.arima.order

Order to use for ARIMA model used in modelling uncertainty on paleoDat (default = c(1,0,0))

summarize

Boolean. Summarize the output? Or return all the ensembles?

Value

a tibble that reports the positivity rate in the synthetics