install.packages('ArArRedux')Once installed, the package can be loaded by typing

library(ArArRedux)This page contains a number of very simple tutorials illustrating the most common usages of the package. Full documentation of the public functions is provided in this PDF document. For this tutorial, we will use the following example input files, which are also built into

Downloads | |

Samples.csv | some ARGUS-VI sample data from the University of Melbourne |

irradiations.csv | the irradiation schedule |

Ca-salt.csv | isotopic analyses of the Ca-interference monitors |

K-glass.csv | isotopic analyses of the K-interference monitors |

Calibration.csv | detector intercalibration data |

AirH1.csv | mass fractionation measurement on detector H1 |

AirAX.csv | mass fractionation measurement on detector AX |

AirL2.csv | mass fractionation measurement on detector L2 |

AirL1.csv | mass fractionation measurement on detector L1 |

masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36") d <- loaddata("Samples.csv",masses) plot(d,"MD2-1a","Ar40")The next example shows the simplest case of a data reduction without interference corrections, detector calibrations or fractionation correction:

# order of the columns in 'Samples.csv' masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36") # prefix of the blanks in 'Samples.csv' blanklabel <- "EXB#" # positions of the fluence monitors in 'Samples.csv' Jpos <- c(3,15) # read the data into a logratio covariance structure X <- read("Samples.csv",masses,blanklabel,Jpos) # load the irradiation schedule (power and duration) irr <- loadirradiations("irradiations.csv") # calculate the ages and their coveriances ages <- process(X,irr) # print a table with ages and standard errors summary(ages)A full example including interference corrections, detector calibrations and fractionation correction:

# see previous example for details about the next three lines masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36") blanklabel <- "EXB#" Jpos <- c(3,15) # order of the columns in Calibration.csv dlabels <- c("H1","AX","L1","L2") X <- read("Samples.csv",masses,blanklabel,Jpos,kfile="K-glass.csv", cafile="Ca-salt.csv",dfile="Calibration.csv",dlabels) irr <- loadirradiations("irradiations.csv") # create a list with fractionation corrections for the denominator isotopes of the age equation fract <- list(Ar37=fractionation("AirL2.csv","L2",PH=TRUE), Ar39=fractionation("AirAX.csv","AX",PH=TRUE) Ar40=fractionation("AirH1.csv","H1",PH=FALSE)) ages <- process(X,irr,fract) summary(ages)In the following example, the interference corrections are specified manually, rather than from co-irradiated K-glass or Ca-salt:

# see previous examples for details of next six lines masses <- c("Ar37","Ar38","Ar39","Ar40","Ar36") blanklabel <- "EXB#" Jpos <- c(3,15) X <- read("Samples.csv",masses,blanklabel,Jpos) irr <- loadirradiations("irradiations.csv") fract <- list(Ar37=fractionation("AirL2.csv","L2",PH=TRUE), Ar39=fractionation("AirAX.csv","AX",PH=TRUE) Ar40=fractionation("AirH1.csv","H1",PH=FALSE)) # assume log(36Ar/37Ar) = log(39Ar/37Ar) in Ca-salt # with variances = 0.0001 and covariances = 0 ca <- interference(intercepts=c(1,1),num=c("Ar39","Ar36"), den=c("Ar37","Ar37"),irr="UM52",label="Ca-salt", covmat=matrix(c(0.001,0,0,0.001),nrow=2)) # assume log(40Ar/39Ar) = -4.637788 in co-irradiated # K-glass with variance 7.9817e-4 k <- interference(intercepts=-4.637788,covmat=7.9817e-4,num="Ar40", den="Ar39",irr="UM52",label="K-glass") ages <- process(X,irr,fract,ca,k) summary(ages)Default parameters, such as the standard age, air ratio or decay constants can be queried or modified using the

# query the air ratio param(X)$air # modify the standard age (ts) and its standard error (sts) Y <- param(X, ts=27.4, sts=0.4) # list all the parameters param(Y)Post processing the results:

# calculate and print the weighted mean of all samples with prefix "FC" print(weightedmean(ages,"FC")) # calculate the weighted mean of a list of samples print(weightedmean(ages,c("MD2-1c","MD2-1d","MD2-1e","MD2-1f","MD2-1g"))) # plot the covariance structure of the ages as a correlation matrix plotcorr(ages)The output of the