Plot function for an RLum.Results S4 class object

The function provides a standardised plot output for data of an RLum.Results S4 class object

plot_RLum.Results(object, single = TRUE, ...)

Arguments

object

RLum.Results (required): S4 object of class RLum.Results

single

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.

Value

Returns multiple plots.

Details

The function produces a multiple plot output. A file output is recommended (e.g., pdf).

Note

Not all arguments available for plot will be passed! Only plotting of RLum.Results objects are supported.

Function version

0.2.1

See also

plot, plot_RLum

Author

Christoph Burow, University of Cologne (Germany)
Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany) , RLum Developer Team

How to cite

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

Examples

###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)