print.summary.bremla: Print summary.bremla class

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

Print summary.bremla class

Description

Prints summary.bremla S3 class.

Usage

## S3 method for class 'summary.bremla'
print(x, digits = 4L, ...)

Arguments

x	summary.bremla S3 class. Output of summary.bremla function
digits	Number of digits displayed.
...	Other arguments

Author(s)

Eirik Myrvoll-Nilsen, [e-mail]

See Also

bremla,bremla_chronology_simulation

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")
control.sim=list(synchronized=2,
                 summary=list(compute=TRUE))

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

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