Description Usage Arguments Details Value Function version How to cite Author(s) Examples
View source: R/calc_Statistics.R
This function calculates a number of descriptive statistics for estimates with a given standard error (SE), most fundamentally using errorweighted approaches.
1 2 
data 

weight.calc 

digits 

n.MCM 

na.rm 

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 MCMsamples 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.
Returns a list with weighted and unweighted statistic measures.
0.1.7 (20170629 18:40:14)
Dietze, M. (2017). calc_Statistics(): Function to calculate statistic measures. Function version 0.1.7. In: Kreutzer, S., Dietze, M., Burow, C., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J. (2017). Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.7.5. https://CRAN.Rproject.org/package=Luminescence
Michael Dietze, GFZ Potsdam (Germany)
R Luminescence Package Team
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## load example data
data(ExampleData.DeValues, envir = environment())
## show a rough plot of the data to illustrate the nonnormal distribution
plot_KDE(ExampleData.DeValues$BT998)
## calculate statistics and show output
str(calc_Statistics(ExampleData.DeValues$BT998))
## Not run:
## 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))
## End(Not run)

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