Explore uncertainty space on abscissa or ordinate and propagate of any of actR's change functions

propagateUncertainty(
  time,
  vals,
  changeFun,
  simulate.time.uncertainty = TRUE,
  simulate.paleo.uncertainty = TRUE,
  n.ens = 100,
  bam.model = list(ns = n.ens, name = "bernoulli", param = 0.05),
  paleo.uncertainty = sd(vals, na.rm = TRUE)/2,
  paleo.ar1 = sqrt(0.5),
  paleo.arima.order = c(1, 0, 0),
  summarize = FALSE,
  seed = round(sum(time, na.rm = TRUE)),
  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
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)
n.ens: How many ensembles to use for error propagation? (default = 100)
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?
seed: set a seed for reproducibility
progress: show null hypothesis testing progress bar?
...: arguments to pass to pass to changeFun

Value

a propagated uncertainty tibble