R/plot_RLum.Results.R
plot_RLum.Results.Rd
The function provides a standardised plot output for data of an RLum.Results S4 class object
plot_RLum.Results(object, single = TRUE, ...)
RLum.Results (required):
S4 object of class RLum.Results
logical (with default):
single plot output (TRUE/FALSE
) to allow for plotting the results in as
few plot windows as possible.
further arguments and graphical parameters will be passed to
the plot
function.
Returns multiple plots.
The function produces a multiple plot output. A file output is recommended (e.g., pdf).
Not all arguments available for plot will be passed!
Only plotting of RLum.Results
objects are supported.
0.2.1
Burow, C., Kreutzer, S., 2023. plot_RLum.Results(): Plot function for an RLum.Results S4 class object. Function version 0.2.1. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., 2023. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.23. https://CRAN.R-project.org/package=Luminescence
###load data
data(ExampleData.DeValues, envir = environment())
# apply the un-logged minimum age model
mam <- calc_MinDose(data = ExampleData.DeValues$CA1, sigmab = 0.2, log = TRUE, plot = FALSE)
#>
#> ----------- meta data -----------
#> n par sigmab logged Lmax BIC
#> 62 3 0.2 TRUE -32.43138 84.14389
#>
#> --- final parameter estimates ---
#> gamma sigma p0 mu
#> 45.64 1.56 0.02 0
#>
#> ------ confidence intervals -----
#> 2.5 % 97.5 %
#> gamma 38.47 53.52
#> sigma 1.34 1.90
#> p0 NA 0.28
#>
#> ------ De (asymmetric error) -----
#> De lower upper
#> 45.64 38.61 53.65
#>
#> ------ De (symmetric error) -----
#> De error
#> 45.64 3.84
##plot
plot_RLum.Results(mam)
# estimate the number of grains on an aliquot
grains<- calc_AliquotSize(grain.size = c(100,150), sample.diameter = 1, plot = FALSE, MC.iter = 100)
#>
#> [calc_AliquotSize]
#>
#> ---------------------------------------------------------
#> mean grain size (microns) : 125
#> sample diameter (mm) : 1
#> packing density : 0.65
#> number of grains : 42
#>
#> --------------- Monte Carlo Estimates -------------------
#> number of iterations (n) : 100
#> median : 41
#> mean : 48
#> standard deviation (mean) : 25
#> standard error (mean) : 2.5
#> 95% CI from t-test (mean) : 43 - 53
#> standard error from CI (mean): 2.6
#> ---------------------------------------------------------
##plot
plot_RLum.Results(grains)