bremla_synchronized_simulation: Simulate synchronized chronologies

In eirikmn/bremla: Bayesian Regression Modeling of Layer-Counted Archives

Simulate synchronized chronologies

Description

Produces simulations from the synchronized time scale given tie-point samples.

Usage

bremla_synchronized_simulation(object, control.sim, print.progress = FALSE)

Arguments

object	Output of function tiepointsimmer.
control.sim	List containing specifications for the simulation procedure, including the number of samples to be generated and whether or not the chronologies should be synchronized (for this function this should be set to TRUE). See control.sim.default for details.
print.progress	Boolean. If TRUE progress will be printed to screen.

Value

Returns the object list from the input and appends a list which includes all synchronized simulations, summary statistics and other information computed or related to the simulation procedure in object\$simulation.

Author(s)

Eirik Myrvoll-Nilsen, [e-mail]

See Also

bremla_chronology_simulation bremla

Examples

if(inlaloader()){
require(stats)
set.seed(1)
n <- 1000
phi <- 0.8
sigma <- 1.2
a_lintrend <- 0.3; a_proxy = 0.8
dy_noise <- as.numeric(arima.sim(model=list(ar=c(phi)),n=n,sd=sqrt(1-phi^2)))
lintrend <- seq(from=10,to=15,length.out=n)

proxy <- as.numeric(arima.sim(model=list(ar=c(0.9)),n=n,sd=sqrt(1-0.9^2)))
dy <- a_lintrend*lintrend + a_proxy*proxy + sigma*dy_noise

y0 = 11700;z0=1200
age = y0+cumsum(dy)
depth = 1200 + 1:n*0.05
depth2 = depth^2/depth[1]^2 #normalize for stability

formula = dy~-1+depth2 + proxy
data = data.frame(age=age,dy=dy,proxy=proxy,depth=depth,depth2=depth2)
data = rbind(c(y0,NA,NA,z0,NA),data) #First row is only used to extract y0 and z0.

events=list(locations=c(1210,1220,1240))
control.fit = list(ncores=2,noise="ar1")
synchronization=list(locations=depth[c(100,400,700)],method="gauss",
                           params=list(mean=c(age[c(100,400,700)]+c(30,-100,50)),
                                       sd=c(50,20,100)
                                       )
                     )
control.sim=list(synchronized=TRUE,
                 summary=list(compute=TRUE))

object = bremla_prepare(formula,data,nsims=5000,reference.label="simulated timescale",
                        events = events,
                        synchronization=synchronization,
                        control.fit=control.fit,
                        control.sim=control.sim)
object = bremla_modelfitter(object)
object = tiepointsimmer(object)
object = bremla_synchronized_simulation(object, print.progress=TRUE)
summary(object)
plot(object)
}

eirikmn/bremla documentation built on Jan. 25, 2025, 4:41 a.m.