This function calculates a number of descriptive statistics for estimates with a given standard error (SE), most fundamentally using error-weighted approaches.
calc_Statistics( data, weight.calc = "square", digits = NULL, n.MCM = NULL, na.rm = TRUE )
data | data.frame or RLum.Results object (required):
for data.frame two columns: De ( |
---|---|
weight.calc | character:
type of weight calculation. One out of |
digits | integer (with default):
round numbers to the specified digits.
If digits is set to |
n.MCM | numeric (with default):
number of samples drawn for Monte Carlo-based statistics.
|
na.rm | logical (with default):
indicating whether |
Returns a list with weighted and unweighted statistic measures.
The option to use Monte Carlo Methods (n.MCM
) allows calculating
all descriptive statistics based on random values. The distribution of these
random values is based on the Normal distribution with De
values as
means and De_error
values as one standard deviation. Increasing the
number of MCM-samples linearly increases computation time. On a Lenovo X230
machine evaluation of 25 Aliquots with n.MCM = 1000 takes 0.01 s, with
n = 100000, ca. 1.65 s. It might be useful to work with logarithms of these
values. See Dietze et al. (2016, Quaternary Geochronology) and the function
plot_AbanicoPlot for details.
0.1.7
Michael Dietze, GFZ Potsdam (Germany) , RLum Developer Team
Dietze, M., 2021. calc_Statistics(): Function to calculate statistic measures. Function version 0.1.7. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., 2021. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.11. https://CRAN.R-project.org/package=Luminescence
## load example data data(ExampleData.DeValues, envir = environment()) ## show a rough plot of the data to illustrate the non-normal distribution plot_KDE(ExampleData.DeValues$BT998)#> List of 3 #> $ weighted :List of 9 #> ..$ n : int 25 #> ..$ mean : num 2896 #> ..$ median : num 2884 #> ..$ sd.abs : num 240 #> ..$ sd.rel : num 8.29 #> ..$ se.abs : num 48 #> ..$ se.rel : num 1.66 #> ..$ skewness: num 1.34 #> ..$ kurtosis: num 4.39 #> $ unweighted:List of 9 #> ..$ n : int 25 #> ..$ mean : num 2951 #> ..$ median : num 2884 #> ..$ sd.abs : num 282 #> ..$ sd.rel : num 9.54 #> ..$ se.abs : num 56.3 #> ..$ se.rel : num 1.91 #> ..$ skewness: num 1.34 #> ..$ kurtosis: num 4.39 #> $ MCM :List of 9 #> ..$ n : int 25 #> ..$ mean : num 2951 #> ..$ median : num 2884 #> ..$ sd.abs : num 282 #> ..$ sd.rel : num 9.54 #> ..$ se.abs : num 56.3 #> ..$ se.rel : num 1.91 #> ..$ skewness: num 1.34 #> ..$ kurtosis: num 4.39if (FALSE) { ## now the same for 10000 normal distributed random numbers with equal errors x <- as.data.frame(cbind(rnorm(n = 10^5, mean = 0, sd = 1), rep(0.001, 10^5))) ## note the congruent results for weighted and unweighted measures str(calc_Statistics(x)) }