Package 'ArArRedux' reference manual

Title:	Rigorous Data Reduction and Error Propagation of Ar40 / Ar39 Data
Description:	Processes noble gas mass spectrometer data to determine the isotopic composition of argon (comprised of Ar36, Ar37, Ar38, Ar39 and Ar40) released from neutron-irradiated potassium-bearing minerals. Then uses these compositions to calculate precise and accurate geochronological ages for multiple samples as well as the covariances between them. Error propagation is done in matrix form, which jointly treats all samples and all isotopes simultaneously at every step of the data reduction process. Includes methods for regression of the time-resolved mass spectrometer signals to t=0 ('time zero') for both single- and multi-collector instruments, blank correction, mass fractionation correction, detector intercalibration, decay corrections, interference corrections, interpolation of the irradiation parameter between neutron fluence monitors, and (weighted mean) age calculation. All operations are performed on the logs of the ratios between the different argon isotopes so as to properly treat them as 'compositional data', sensu Aitchison [1986, The Statistics of Compositional Data, Chapman and Hall].
Authors:	Pieter Vermeesch [aut, cre]
Maintainer:	Pieter Vermeesch <[email protected]>
License:	GPL-2
Version:	1.0
Built:	2024-11-17 06:25:31 UTC
Source:	https://github.com/pvermees/ararredux

Calculate the arithmetic mean

Description

Calculate the arithmetic mean of some logratio data

Usage

average(x, i = NULL, newlabel = NULL)
average(x, i = NULL, newlabel = NULL)

Arguments

`x`	an object of class `redux` or `logratios`
`i`	(optional) vector of sample indices
`newlabel`	(optional) string with the new label to be assigned to the averaged values

Value

an object of the same class as x

Examples

data(Melbourne)
K <- average(Melbourne$X,grep("K:",Melbourne$X$labels),newlabel="K-glass")
plotcorr(K)
data(Melbourne)
K <- average(Melbourne$X,grep("K:",Melbourne$X$labels),newlabel="K-glass")
plotcorr(K)

Average all the data collected on the same day.

Description

This function is useful for grouping a number of replicate air shots or calibration experiments

Usage

averagebyday(x, newlabel)
averagebyday(x, newlabel)

Arguments

`x`	an object of class `timeresolved`, `logratios`, `PHdata` or `redux`
`newlabel`	a string with the new label that should be given to the average

Value

an object of the same class as x

Examples

dfile <- system.file("Calibration.csv",package="ArArRedux")
dlabels <- c("H1","AX","L1","L2")
md <- loaddata(dfile,dlabels,PH=TRUE)
ld <- fitlogratios(blankcorr(md))
d <- averagebyday(ld,"DCAL")
plotcorr(d)
dfile <- system.file("Calibration.csv",package="ArArRedux")
dlabels <- c("H1","AX","L1","L2")
md <- loaddata(dfile,dlabels,PH=TRUE)
ld <- fitlogratios(blankcorr(md))
d <- averagebyday(ld,"DCAL")
plotcorr(d)

Apply a blank correction

Description

Applies a blank correction to some time-resolved mass spectrometer data

Usage

blankcorr(x, ...)

## Default S3 method:
blankcorr(x, ...)

## S3 method for class 'timeresolved'
blankcorr(x, blanklabel = NULL, prefix = "", ...)

## S3 method for class 'PHdata'
blankcorr(x, blanklabel = NULL, prefix = "", ...)
blankcorr(x, ...)

## Default S3 method:
blankcorr(x, ...)

## S3 method for class 'timeresolved'
blankcorr(x, blanklabel = NULL, prefix = "", ...)

## S3 method for class 'PHdata'
blankcorr(x, blanklabel = NULL, prefix = "", ...)

Arguments

`x`	an object of class `timeresolved` or `PHdata`
`...`	other arguments
`blanklabel`	as string denoting the prefix of the blanks
`prefix`	a string to be prepended to the non-blanks

Value

an object of class blankcorrected

Examples

samplefile <- system.file("Samples.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
m <- loaddata(samplefile,masses) # samples and J-standards
blanklabel <- "EXB#"
l <- fitlogratios(blankcorr(m,blanklabel),"Ar40")
plotcorr(l)
samplefile <- system.file("Samples.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
m <- loaddata(samplefile,masses) # samples and J-standards
blanklabel <- "EXB#"
l <- fitlogratios(blankcorr(m,blanklabel),"Ar40")
plotcorr(l)

The `blankcorrected` class

Description

An object class containing blank-corrected mass spectrometry data

Details

Extends the class classes timeresolved and PHdata by adding an additional list item blankindices containg the index of the nearest blank. fitlogratios uses this information to group the samples during regression to 'time zero'.

Detector calibration

Description

Apply the detector calibration for multicollector data

Usage

calibration(X, clabel)
calibration(X, clabel)

Arguments

`X`	an object of class `redux`
`clabel`	the label of the detector calibration data

Value

an object of class redux

Examples

data(Melbourne)
C <- calibration(Melbourne$X,"DCAL")
plotcorr(C)
data(Melbourne)
C <- calibration(Melbourne$X,"DCAL")
plotcorr(C)

Cl-interference correction

Description

Apply the interference correction for the Cl-decay products

Usage

clcorrection(X, irr)
clcorrection(X, irr)

Arguments

`X`	an object of class `redux`
`irr`	the irradiation schedule

Value

an object of class redux

Examples

data(Melbourne)
Cl <- clcorrection(Melbourne$X,Melbourne$irr)
plotcorr(Cl)
data(Melbourne)
Cl <- clcorrection(Melbourne$X,Melbourne$irr)
plotcorr(Cl)

Merge a list of logratio data

Description

Recursively concatenates a list of logratio data into one big dataset

Usage

concat(lrlist)
concat(lrlist)

Arguments

lrlist

a list containing items of class logratios or redux

Value

an object of the same class as x containing the merged dataset

Examples

samplefile <-  system.file("Samples.csv",package="ArArRedux")
kfile <- system.file("K-glass.csv",package="ArArRedux")
cafile <- system.file("Ca-salt.csv",package="ArArRedux")
dfile <- system.file("Calibration.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
blanklabel <- "EXB#"
Jpos <- c(3,15)
dlabels <- c("H1","AX","L1","L2")
 
m <- loaddata(samplefile,masses) # samples and J-standards
mk <- loaddata(kfile,masses) # K-interference data
mca <- loaddata(cafile,masses) # Ca interference data
md <- loaddata(dfile,dlabels,PH=TRUE) # detector intercalibrations
 
# form and fit logratios
l <- fitlogratios(blankcorr(m,blanklabel),"Ar40")
lk <- fitlogratios(blankcorr(mk,blanklabel,"K:"),"Ar40")
k <- getmasses(lk,"Ar39","Ar40") # subset on the relevant isotopes
lca <- fitlogratios(blankcorr(mca,blanklabel,"Ca:"),"Ar37")
ca <- getmasses(lca,c("Ar36","Ar39"),c("Ar37","Ar37")) # subset
ld <- fitlogratios(blankcorr(md))
d <- averagebyday(ld,"DCAL")

# merge all data (except air shots) into one big logratio structure
X <- newredux(concat(list(l,k,ca,d)),Jpos)
data(Melbourne)
if (isTRUE(all.equal(Melbourne$X,X))) {
   print("We just reconstructed the built-in dataset Melbourne$X")}
samplefile <-  system.file("Samples.csv",package="ArArRedux")
kfile <- system.file("K-glass.csv",package="ArArRedux")
cafile <- system.file("Ca-salt.csv",package="ArArRedux")
dfile <- system.file("Calibration.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
blanklabel <- "EXB#"
Jpos <- c(3,15)
dlabels <- c("H1","AX","L1","L2")
 
m <- loaddata(samplefile,masses) # samples and J-standards
mk <- loaddata(kfile,masses) # K-interference data
mca <- loaddata(cafile,masses) # Ca interference data
md <- loaddata(dfile,dlabels,PH=TRUE) # detector intercalibrations
 
# form and fit logratios
l <- fitlogratios(blankcorr(m,blanklabel),"Ar40")
lk <- fitlogratios(blankcorr(mk,blanklabel,"K:"),"Ar40")
k <- getmasses(lk,"Ar39","Ar40") # subset on the relevant isotopes
lca <- fitlogratios(blankcorr(mca,blanklabel,"Ca:"),"Ar37")
ca <- getmasses(lca,c("Ar36","Ar39"),c("Ar37","Ar37")) # subset
ld <- fitlogratios(blankcorr(md))
d <- averagebyday(ld,"DCAL")

# merge all data (except air shots) into one big logratio structure
X <- newredux(concat(list(l,k,ca,d)),Jpos)
data(Melbourne)
if (isTRUE(all.equal(Melbourne$X,X))) {
   print("We just reconstructed the built-in dataset Melbourne$X")}

Correct for radioactive decay occurred since irradiation

Description

Correct for radioactive decay of neutron-induced 37Ar and 39Ar occurred since irradiation

Usage

decaycorrection(X, irr, isotope)
decaycorrection(X, irr, isotope)

Arguments

`X`	an objects of class `redux`
`irr`	the irradiation schedule
`isotope`	a string denoting the isotope that needs correcting

Value

an object of class redux

Examples

data(Melbourne)
C <- calibration(Melbourne$X,"DCAL")
A <- massfractionation(C,Melbourne$fract)
D9 <- decaycorrection(A,Melbourne$irr,"Ar39")
plotcorr(D9)
data(Melbourne)
C <- calibration(Melbourne$X,"DCAL")
A <- massfractionation(C,Melbourne$fract)
D9 <- decaycorrection(A,Melbourne$irr,"Ar39")
plotcorr(D9)

Extrapolation to 'time zero'

Description

This function extrapolates time resolved mass spectrometer data to t=0. When fed with multicollector data, it forms the ratios of the raw signals, forms their logs and performs linear regression to t=0 When fed with single collector data, the function first takes their logs and extrapolates them to t=0 before taking ratios, unless denmass=NULL, in which case the logs of the raw signals are extrapolated.

Usage

fitlogratios(x, ...)

## Default S3 method:
fitlogratios(x, ...)

## S3 method for class 'timeresolved'
fitlogratios(x, denmass, ...)

## S3 method for class 'PHdata'
fitlogratios(x, denmass = NULL, ...)
fitlogratios(x, ...)

## Default S3 method:
fitlogratios(x, ...)

## S3 method for class 'timeresolved'
fitlogratios(x, denmass, ...)

## S3 method for class 'PHdata'
fitlogratios(x, denmass = NULL, ...)

Arguments

`x`	an object of class `timeresolved` or `PHdata`
`...`	further arguments (see below)
`denmass`	a string denoting the denominator isotope

Value

an object of class logratios

Examples

samplefile <- system.file("Samples.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
m <- loaddata(samplefile,masses) # samples and J-standards
blanklabel <- "EXB#"
l <- fitlogratios(blankcorr(m,blanklabel),"Ar40")
plotcorr(l)
samplefile <- system.file("Samples.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
m <- loaddata(samplefile,masses) # samples and J-standards
blanklabel <- "EXB#"
l <- fitlogratios(blankcorr(m,blanklabel),"Ar40")
plotcorr(l)

Compute the mass fractionation correction

Description

Compares the measured 40Ar/36Ar ratio of an air shot on a given detector with the atmospheric ratio.

Usage

fractionation(fname, detector, MS = "ARGUS-VI", PH = FALSE)
fractionation(fname, detector, MS = "ARGUS-VI", PH = FALSE)

Arguments

`fname`	a .csv file with the air shot data
`detector`	the name of the ion detector
`MS`	the type of mass spectrometer
`PH`	TRUE if the data were recorded in 'peak hopping' mode, FALSE if recorded in multicollector mode.

Value

an object of class logratios

Examples

data(Melbourne)
fd37file <- system.file("AirL2.csv",package="ArArRedux")
fd40file <- system.file("AirH1.csv",package="ArArRedux")
fract <- list(fractionation(fd37file,"L2",PH=TRUE),
              fractionation(fd40file,"H1",PH=FALSE))
if (isTRUE(all.equal(Melbourne$fract,fract))){
  print("We just re-created the fractionation correction for the Melbourne dataset")
}
data(Melbourne)
fd37file <- system.file("AirL2.csv",package="ArArRedux")
fd40file <- system.file("AirH1.csv",package="ArArRedux")
fract <- list(fractionation(fd37file,"L2",PH=TRUE),
              fractionation(fd40file,"H1",PH=FALSE))
if (isTRUE(all.equal(Melbourne$fract,fract))){
  print("We just re-created the fractionation correction for the Melbourne dataset")
}

Calculate the 40Ar*/39ArK-ratios

Description

Calculate the 40Ar*/39ArK-ratios of interference corrected logratio intercept data

Usage

get4039(X, irr)
get4039(X, irr)

Arguments

`X`	an object of class `redux` containing some interference corrected logratio intercept data
`irr`	the irradiation schedule

Value

an object of class link{redux} containing the 40Ar*/39ArK-ratios as intercepts and its covariance matrix as covmat

Examples

data(Melbourne)
R <- get4039(Melbourne$X,Melbourne$irr)
plotcorr(R)
data(Melbourne)
R <- get4039(Melbourne$X,Melbourne$irr)
plotcorr(R)

Calculate 40Ar/39Ar ages

Description

Calculate 40Ar/39Ar ages from a vector of 40Ar/39Ar-ratios and J-factors

Usage

getages(RJ)
getages(RJ)

Arguments

`RJ`	an object of class `Redux` containing the amalgamated $^40$Ar$^*$/$^39$Ar$_K$-ratios and J-factors with their covariance matrix

Value

an object of class results containing the ages and their covariance matrix

Examples

data(Melbourne)
R <- get4039(Melbourne$X,Melbourne$irr)
J <- getJfactors(R)
ages <- getages(J)
plotcorr(ages)
data(Melbourne)
R <- get4039(Melbourne$X,Melbourne$irr)
J <- getJfactors(R)
ages <- getages(J)
plotcorr(ages)

Calculate the irradiation parameter ('J factor')

Description

Interpolate the irradiation parameters for the samples given the 40Ar*/39ArK ratios of the samples and fluence monitors

Usage

getJfactors(R)
getJfactors(R)

Arguments

`R`	a vector of 40Ar*/39ArK ratios

Value

an object of class redux containing, as intercepts, the 40Ar*/39ArK ratios of the samples, the interpolated J-factors, and the 40K decay constant; and as covmat: the covariance matrix. All other class properties are inherited from R.

Examples

data(Melbourne)
R <- get4039(Melbourne$X,Melbourne$irr)
J <- getJfactors(R)
plotcorr(J)
data(Melbourne)
R <- get4039(Melbourne$X,Melbourne$irr)
J <- getJfactors(R)
plotcorr(J)

Select a subset of isotopes from a dataset

Description

Extracts the intercepts, covariance matrix, etc. of a selection of isotopes from a larger dataset

Usage

getmasses(x, ...)

## Default S3 method:
getmasses(x, ...)

## S3 method for class 'timeresolved'
getmasses(x, mass, invert = FALSE, ...)

## S3 method for class 'logratios'
getmasses(x, num, den, invert = FALSE, ...)

## S3 method for class 'redux'
getmasses(x, num, den, invert = FALSE, ...)
getmasses(x, ...)

## Default S3 method:
getmasses(x, ...)

## S3 method for class 'timeresolved'
getmasses(x, mass, invert = FALSE, ...)

## S3 method for class 'logratios'
getmasses(x, num, den, invert = FALSE, ...)

## S3 method for class 'redux'
getmasses(x, num, den, invert = FALSE, ...)

Arguments

`x`	an object of class `logratios`, `timeresolved`, `PHdata` or `redux`.
`...`	other arguments
`mass`	a vector of strings denoting the masses of interest
`invert`	boolean parameter indicating whether the selection should be inverted (default = FALSE)
`num`	vector of strings indicating the numerator isotopes
`den`	vector of string indicating the denominator isotopes

Value

an object of the same class as x

Examples

kfile <- system.file("K-glass.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
mk <- loaddata(kfile,masses)
lk <- fitlogratios(blankcorr(mk,"EXB#","K:"),"Ar40")
k <- getmasses(lk,"Ar39","Ar40") # subset of the relevant isotopes
plotcorr(k)
kfile <- system.file("K-glass.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
mk <- loaddata(kfile,masses)
lk <- fitlogratios(blankcorr(mk,"EXB#","K:"),"Ar40")
k <- getmasses(lk,"Ar39","Ar40") # subset of the relevant isotopes
plotcorr(k)

define the interference corrections

Description

create a new object of class logratios containing the interferences from neutron reactions on Ca and K

Usage

interference(intercepts, covmat, num, den, irr, label)
interference(intercepts, covmat, num, den, irr, label)

Arguments

`intercepts`	a vector with logratios
`covmat`	the covariance matrix of the logratios
`num`	a vector of strings marking the numerator isotopes of `intercepts`
`den`	a vector of strings marking the denominator isotopes of `intercepts`
`irr`	an object of class `irradiations`
`label`	a string with a name which can be used to identify the interference data in subsequent calculations

Value

an object of class logratios

Examples

samplefile <- system.file("Samples.csv",package="ArArRedux")
irrfile <- system.file("irradiations.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
X <- read(samplefile,masses,blabel="EXB#",Jpos=c(3,15))
irr <- loadirradiations(irrfile)
# assume log(36Ar/37Ar) = log(39Ar/37Ar) = 1 in co-irradiate Ca-salt
# with variances of 0.0001 and zero covariances
ca <- interference(intercepts=c(1,1),
                   covmat=matrix(c(0.001,0,0,0.001),nrow=2),
                   num=c("Ar39","Ar36"),den=c("Ar37","Ar37"),
                   irr=X$irr[1],label="Ca-salt")
# assume log(39Ar/40Ar) = 4.637788 in co-irradiate K-glass
# with variance 7.9817e-4
k <- interference(intercepts=4.637788,covmat=7.9817e-4,
                  num="Ar39",den="Ar40",irr=X$irr[1],
                  label="K-glass")
ages <- process(X,irr,ca=ca,k=k)
summary(ages)
samplefile <- system.file("Samples.csv",package="ArArRedux")
irrfile <- system.file("irradiations.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
X <- read(samplefile,masses,blabel="EXB#",Jpos=c(3,15))
irr <- loadirradiations(irrfile)
# assume log(36Ar/37Ar) = log(39Ar/37Ar) = 1 in co-irradiate Ca-salt
# with variances of 0.0001 and zero covariances
ca <- interference(intercepts=c(1,1),
                   covmat=matrix(c(0.001,0,0,0.001),nrow=2),
                   num=c("Ar39","Ar36"),den=c("Ar37","Ar37"),
                   irr=X$irr[1],label="Ca-salt")
# assume log(39Ar/40Ar) = 4.637788 in co-irradiate K-glass
# with variance 7.9817e-4
k <- interference(intercepts=4.637788,covmat=7.9817e-4,
                  num="Ar39",den="Ar40",irr=X$irr[1],
                  label="K-glass")
ages <- process(X,irr,ca=ca,k=k)
summary(ages)

Load mass spectrometer data

Description

Loads a .csv file with raw mass spectrometer data

Usage

loaddata(fname, masses, MS = "ARGUS-VI", PH = FALSE)
loaddata(fname, masses, MS = "ARGUS-VI", PH = FALSE)

Arguments

`fname`	the file name, must end with .csv
`masses`	a vector of strings denoting the order of the isotopes listed in the table
`MS`	the type of mass spectrometer
`PH`	a boolean indicating whether the data are to be treated as multicollector (PH=FALSE) or 'peak hopping' (PH=TRUE) data. The default is PH=FALSE.

Value

if PH=FALSE: an object of class timeresolved
if PH=TRUE: an object of class PHdata

Examples

samplefile <- system.file("Samples.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
m <- loaddata(samplefile,masses) # samples and J-standards
plot(m,"MD2-1a","Ar40")
samplefile <- system.file("Samples.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
m <- loaddata(samplefile,masses) # samples and J-standards
plot(m,"MD2-1a","Ar40")

Load the irradiation schedule

Description

Loads a .csv file with the schedule of a multi-stage neutron irradiation

Usage

loadirradiations(fname)
loadirradiations(fname)

Arguments

fname

file name (in .csv format)

Value

a list of irradiations, where each irradiation is a named list containing:

tin: vector with the start times of irradiations
tout: vector with the end times of irradiations
P: vector with the power of the irradiations

Examples

irrfile <- system.file("irradiations.csv",package="ArArRedux")
irr <- loadirradiations(irrfile)
str(irr)
irrfile <- system.file("irradiations.csv",package="ArArRedux")
irr <- loadirradiations(irrfile)
str(irr)

The `logratios` class

Description

An object class containing logratio intercepts

Details

A list with the following items:

labels: a vector of strings denoting the names of the runs
num: a vector of strings denoting the numerator isotopes
den: a vector of strings denoting the denominator isotopes
intercepts: a vector of logratio intercepts or values
covmat: the covariance matrix of intercepts
irr: a vector of strings denoting the irradiation runs
pos: a vector of integers with the positions in the irradiation stack
thedate: a vector containing the acquisition dates and times
nlr: a vector with the number of logratios per run

Apply the mass fractionation correction

Description

Applies the fractionation obtained from air shot data by fractionation to the denominator detector in order to correct it for the mass difference between the numerator and denominator isotopes.

Usage

massfractionation(X, fract)
massfractionation(X, fract)

Arguments

`X`	an object of class `redux`
`fract`	a list with fractionation data for Ar37, Ar39 and Ar40

Value

an object of class redux

Examples

data(Melbourne)
C <- calibration(Melbourne$X,"DCAL")
A <- massfractionation(C,Melbourne$fract)
plotcorr(A)
data(Melbourne)
C <- calibration(Melbourne$X,"DCAL")
A <- massfractionation(C,Melbourne$fract)
plotcorr(A)

An example dataset

Description

Contains all the relevant information needed for the data reduction some ARGUS-IV data from the University of Melbourne

Author(s)

David Philips [email protected]

Examples

data(Melbourne)
plotcorr(Melbourne$X)
data(Melbourne)
plotcorr(Melbourne$X)

Create a new `redux` object

Description

Initialises a new redux object by packing a logratios dataset together with all the parameters needed for age calculation

Usage

newredux(X, Jpos, detectors = list(Ar36 = "H1", Ar37 = "L2", Ar38 = "L1", Ar39
  = "AX", Ar40 = "H1"))
newredux(X, Jpos, detectors = list(Ar36 = "H1", Ar37 = "L2", Ar38 = "L1", Ar39
  = "AX", Ar40 = "H1"))

Arguments

`X`	an object of class `logratios`
`Jpos`	a vector of integers denoting the positions of the fluence monitors in the irradiation stack
`detectors`	a list of strings denoting the detectors for each argon isotope

Value

an object of class redux

Set or get Ar-Ar_Redux parameters

Description

This function is used to query and modify the half lives, standard ages etc. associated with an object of class redux

Usage

param(X, ...)
param(X, ...)

Arguments

`X`	an object of class `redux`
`...`	any combination of the parameters given below

Details

param grants access to the following parameters:

l0: 40K decay constant (default value = 5.5492e-4 Ma-1, Renne et al. [2010])
sl0: standard error of the 40K decay constant (default value = 0.0047e-4 Ma-1)
l7: 37Ar decay constant (default value = 7.2438 yr-1, Renne and Norman [2001])
sl7: standard error of the 37Ar decay constant (default value = 0.0083 yr-1)
l9: 39Ar decay constant (0.002577 yr-1 Stoenner et al. [1965])
sl9: standard error of the 39Ar decay constant (0.000014 yr-1)
l6: 36Cl decay constant (default value = 2301.3e-9 yr-1)
sl6: standard error of the 36Cl decay constant (default value = 7.6e-9 yr-1
pcl: (36Cl/38Cl)-production rate (default value = 252.7 for OSTR reactor, Renne et al. [2008])
spcl: standard error of the (36Cl/38Cl)-production rate (default value = 1.8)
ts: age of the fluence monitor (default = 28.201 Myr for the Fish Canyon Tuff, Kuiper et al. [2008])
sts: standard error of the fluence monitor age (default value = 0.023 Myr)
air: atmospheric 40Ar/36Ar ratio (default value = 298.56, Lee et al. [2006])
sair: standard error of the atmospheric 40Ar/36Ar ratio (default value = 0.155)

Value

returns the modified redux object OR the current parameter values if no optional arguments are supplied.

Examples

data(Melbourne)
param(Melbourne$X)$air
Y <- param(Melbourne$X,air=295.5)
param(Y)$air
data(Melbourne)
param(Melbourne$X)$air
Y <- param(Melbourne$X,air=295.5)
param(Y)$air

The `PHdata` class

Description

An object class containing time resolved 'peak-hopping' mass spectrometry data

Details

A list with the following items:

masses: a vector of strings denoting the isotopes monitored in each run
signals: a list with objects of class timeresolved, each corresponding to a detector (i.e. length(signals)==1 for single collector instruments).

Plot a time resolved mass spectrometry signal

Description

Plots the raw signal of a given isotope against time.

Usage

## S3 method for class 'timeresolved'
plot(x, label, mass, ...)

## S3 method for class 'PHdata'
plot(x, label, mass, ...)
## S3 method for class 'timeresolved'
plot(x, label, mass, ...)

## S3 method for class 'PHdata'
plot(x, label, mass, ...)

Arguments

`x`	an object of class `timeresolved` or `PHdata`
`label`	a string with the name of the run
`mass`	a string indicating the isotope of interest
`...`	optional parameters

Examples

samplefile <- system.file("Samples.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
mMC <- loaddata(samplefile,masses)
plot(mMC,"MD2-1a","Ar40")
mPH <- loaddata(samplefile,masses,PH=TRUE)
plot(mPH,"MD2-1a","Ar40")
samplefile <- system.file("Samples.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
mMC <- loaddata(samplefile,masses)
plot(mMC,"MD2-1a","Ar40")
mPH <- loaddata(samplefile,masses,PH=TRUE)
plot(mPH,"MD2-1a","Ar40")

Plot a matrix with correlation coefficients

Description

Converts the covariance matrix to a correlation matrix and plots this is a coloured image for visual inspection.

Usage

plotcorr(X)
plotcorr(X)

Arguments

`X`	a data structure (list) containing an item called ‘covmat’ (covariance matrix)

Examples

data(Melbourne)
plotcorr(Melbourne$X)
data(Melbourne)
plotcorr(Melbourne$X)

Process logratio data and calculate 40Ar/39Ar ages

Description

Performs detector calibration, mass fractionation correction, decay corrections, interference corrections, interpolates J-factors and calculates ages.

Usage

process(X, irr, fract = NULL, ca = NULL, k = NULL)
process(X, irr, fract = NULL, ca = NULL, k = NULL)

Arguments

`X`	an object of class `redux`
`irr`	the irradiation schedule
`fract`	list with air shot data (one item per denominator isotope)
`ca`	an object of class `logratios` with Ca-interference data (not necessary if interferences are included in X)
`k`	an object of class `logratios` with K-interference data (not necessary if interferences are included in X)

Examples

data(Melbourne)
ages <- process(Melbourne$X,Melbourne$irr,Melbourne$fract)
summary(ages)
data(Melbourne)
ages <- process(Melbourne$X,Melbourne$irr,Melbourne$fract)
summary(ages)

Read mass spectrometer data

Description

Reads raw mass spectrometer data and parses it into a redux format for further processing.

Usage

read(xfile, masses, blabel, Jpos, kfile = NULL, cafile = NULL,
  dfile = NULL, dlabels = NULL, MS = "ARGUS-VI")
read(xfile, masses, blabel, Jpos, kfile = NULL, cafile = NULL,
  dfile = NULL, dlabels = NULL, MS = "ARGUS-VI")

Arguments

`xfile`	a .csv file with samples and fluence monitor data
`masses`	a list which specifies the order in which the isotopes are recorded by the mass spectrometer
`blabel`	a prefix string denoting the blanks
`Jpos`	a vector of integers denoting the positions of the fluence monitors in the irradiation stack
`kfile`	a .csv file with the K-interference monitor data (optional)
`cafile`	a .csv file with the Ca-interference monitor data (optional)
`dfile`	a .csv file with the detector calibration data (optional)
`dlabels`	a list which specifies the names of the detectors and the order in which they were recorded by the mass spectrometer
`MS`	a string denoting the type of mass spectrometer. At the moment only the ARGUS-IV is listed. Please contact the author to add other file formats to Ar-Ar_Redux.

Value

an object of class redux.

Examples

samplefile <-  system.file("Samples.csv",package="ArArRedux")
kfile <- system.file("K-glass.csv",package="ArArRedux")
cafile <- system.file("Ca-salt.csv",package="ArArRedux")
dfile <- system.file("Calibration.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
dlabels <- c("H1","AX","L1","L2")
X <- read(samplefile,masses,blabel="EXB#",Jpos=c(3,15),
          kfile,cafile,dfile,dlabels)
plotcorr(X)
samplefile <-  system.file("Samples.csv",package="ArArRedux")
kfile <- system.file("K-glass.csv",package="ArArRedux")
cafile <- system.file("Ca-salt.csv",package="ArArRedux")
dfile <- system.file("Calibration.csv",package="ArArRedux")
masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36")
dlabels <- c("H1","AX","L1","L2")
X <- read(samplefile,masses,blabel="EXB#",Jpos=c(3,15),
          kfile,cafile,dfile,dlabels)
plotcorr(X)

The `redux` class

Description

An object class that is used throughout Ar-Ar_Redux

Details

A list with the following items:

Export `ArArRedux` data to `IsoplotR`

Description

Creates a data object compatible with the IsoplotR package

Usage

redux2isoplotr(x, irr, fract = NULL, ca = NULL, k = NULL, format = 1,
  file = NULL)
redux2isoplotr(x, irr, fract = NULL, ca = NULL, k = NULL, format = 1,
  file = NULL)

Arguments

`x`	an object of class `redux`
`irr`	the irradiation schedule
`fract`	list with air shot data (one item per denominator isotope)
`ca`	an object of class `logratios` with Ca-interference data (not necessary if interferences are included in X)
`k`	an object of class `logratios` with K-interference data (not necessary if interferences are included in X)
`format`	input format for `IsoplotR`. I.e. one of `1`: 39/40, s[39/40], 36/40, s[36/40], 39/36, s[39/36] (other formats will be added later)
`file`	optional (`.csv`) file name to write the output to.

Value

an object of class ArAr, i.e. a table with the following columns: 'Ar4036', 'errAr4036', 'Ar3936', 'errAr3936', 'Ar4039', and 'errAr4039'

Examples

data(Melbourne)
print(redux2isoplotr(Melbourne$X,Melbourne$irr))
data(Melbourne)
print(redux2isoplotr(Melbourne$X,Melbourne$irr))

The `results` class

Description

A list with the following items:

Details

labels: a vector of strings denoting the names of the runs
intercepts: a vector of ages
covmat: the covariance matrix of intercepts
thedate: a vector containing the acquisition dates and times

Select a subset of some data

Description

Extracts those intercepts, covariances etc. that match a given list of indices or labels.

Usage

## S3 method for class 'timeresolved'
subset(x, i = NULL, labels = NULL, invert = FALSE,
  include.J = FALSE, ...)

## S3 method for class 'logratios'
subset(x, i = NULL, labels = NULL, invert = FALSE,
  include.J = FALSE, ...)

## S3 method for class 'redux'
subset(x, i = NULL, labels = NULL, invert = FALSE,
  include.J = FALSE, ...)

## S3 method for class 'results'
subset(x, i = NULL, labels = NULL, invert = FALSE, ...)
## S3 method for class 'timeresolved'
subset(x, i = NULL, labels = NULL, invert = FALSE,
  include.J = FALSE, ...)

## S3 method for class 'logratios'
subset(x, i = NULL, labels = NULL, invert = FALSE,
  include.J = FALSE, ...)

## S3 method for class 'redux'
subset(x, i = NULL, labels = NULL, invert = FALSE,
  include.J = FALSE, ...)

## S3 method for class 'results'
subset(x, i = NULL, labels = NULL, invert = FALSE, ...)

Arguments

`x`	an object of class `timeresolved`, `logratios`, `redux` or `results`
`i`	a vector with indices of the selected runs
`labels`	a string or a vector of strings with sample names
`invert`	boolean flag indicating whether the selection should be inverted, i.e. whether the selected indices or labels should be removed rather than retained
`include.J`	if `TRUE`, automatically adds the irradiation monitors to the selection
`...`	other arguments

Value

an object of the same class as x

Examples

data(Melbourne)
ages <- process(Melbourne$X,Melbourne$irr,Melbourne$fract)
MD <- subset(ages,labels=c("MD2-1","MD2-2","MD2-3","MD2-4","MD2-5"))
plotcorr(MD)
data(Melbourne)
ages <- process(Melbourne$X,Melbourne$irr,Melbourne$fract)
MD <- subset(ages,labels=c("MD2-1","MD2-2","MD2-3","MD2-4","MD2-5"))
plotcorr(MD)

Summary table

Description

Plots the ages and their standard errors

Usage

## S3 method for class 'results'
summary(object, ...)
## S3 method for class 'results'
summary(object, ...)

Arguments

`object`	an objct of class `results`
`...`	no other arguments

Examples

data(Melbourne)
ages <- process(Melbourne$X,Melbourne$irr,Melbourne$fract)
summary(ages)[1:5,]
data(Melbourne)
ages <- process(Melbourne$X,Melbourne$irr,Melbourne$fract)
summary(ages)[1:5,]

The `timeresolved` class

Description

An object class containing time resolved multi-collector mass spectrometry data

Details

A list with the following items:

masses: a vector of strings denoting the isotopes monitored in each run
irr: a vector of strings denoting the irradiation runs
pos: a vector of integers with the positions in the irradiation stack
thedate: a vector containing the acquisition dates and times
d: a data table
thetime: a matrix with the measurement times

Calculate the weighted mean age

Description

Computes the error weighted mean and MSWD of some samples taking into covariances.

Usage

weightedmean(ages, prefix = NULL)
weightedmean(ages, prefix = NULL)

Arguments

`ages`	an object of class `results`
`prefix`	is either a string with the prefix of the samples that need to be averaged, or a vector of sample names.

Value

a list with items:

avgt: the weighted mean age
err: the standard error of avgt
MSWD: the Mean Square of the Weighted Deviates

Examples

data(Melbourne)
ages <- process(Melbourne$X,Melbourne$irr,Melbourne$fract)
weightedmean(ages,"MD2-")
data(Melbourne)
ages <- process(Melbourne$X,Melbourne$irr,Melbourne$fract)
weightedmean(ages,"MD2-")

Package 'ArArRedux'

Help Index

Calculate the arithmetic mean

Description

Usage

Arguments

Value

Examples

Average all the data collected on the same day.

Description

Usage

Arguments

Value

Examples

Apply a blank correction

Description

Usage

Arguments

Value

Examples

The blankcorrected class

Description

Details

Detector calibration

Description

Usage

Arguments

Value

Examples

Cl-interference correction

Description

Usage

Arguments

Value

Examples

Merge a list of logratio data

Description

Usage

Arguments

Value

Examples

Correct for radioactive decay occurred since irradiation

Description

Usage

Arguments

Value

Examples

Extrapolation to 'time zero'

Description

Usage

Arguments

Value

Examples

Compute the mass fractionation correction

Description

Usage

Arguments

Value

Examples

Calculate the 40Ar*/39ArK-ratios

Description

Usage

Arguments

Value

Examples

Calculate 40Ar/39Ar ages

Description

Usage

Arguments

Value

Examples

Calculate the irradiation parameter ('J factor')

Description

Usage

Arguments

Value

Examples

Select a subset of isotopes from a dataset

Description

Usage

The `blankcorrected` class

The `logratios` class

Create a new `redux` object

The `PHdata` class