R/ensembleComposite.R
createConcatenatedEnsembleMeasurementTable.Rd
This "shuffles" together two *independent* ensemble datasets, making no assumptions about stratigraphic order, but assuming that each dataset is representing the same phenomenon.
createConcatenatedEnsembleMeasurementTable(
age.ens.1,
paleo.data.1,
age.ens.2,
paleo.data.2,
n.ens = 1000
)
A measurement table object
Other LiPD manipulation:
createModel()
,
createMultiModelEnsemble()
,
createSummaryTableFromEnsembleTable()
,
createTSid()
,
estimateUncertaintyFromRange()
,
getVariableIndex()
,
mapAgeEnsembleToPaleoData()
,
pullTsVariable()
,
selectData()
if (FALSE) { # \dontrun{
library(lipdR)
library(geoChronR)
library(magrittr)
L1 <- readLipd("~/Downloads/ANS1.Kjellman.2019.lpd")
L2 <- readLipd("~/Downloads/ANP3.Kjellman.2019.lpd")
#composite two independent records
L1 <- mapAgeEnsembleToPaleoData(L1,paleo.meas.table.num = 1,age.var = "ageEnsemble")
age.ens.1 <- selectData(L1,"ageEnsemble",meas.table.num = 1)
paleo.data.1 <- selectData(L1,"C28d2H",,meas.table.num = 1)
L2 <- mapAgeEnsembleToPaleoData(L2,paleo.meas.table.num = 1,age.var = "ageEnsemble")
age.ens.2 <- selectData(L2,"ageEnsemble",meas.table.num = 1)
paleo.data.2 <- selectData(L2,"C28d2H",,meas.table.num = 1)
cmt <- createConcatenatedEnsembleMeasurementTable(age.ens.1,paleo.data.1,age.ens.2,paleo.data.2)
plotTimeseriesEnsRibbons(X = cmt$ageEnsemble,Y = cmt$C28d2HComposite) %>%
plotTimeseriesEnsLines(X = cmt$ageEnsemble,Y = cmt$C28d2HComposite,n.ens.plot = 5,color = "red")
} # }