Luminescence (version 0.6.1)

calc_Statistics: Function to calculate statistic measures

Description

This function calculates a number of descriptive statistics for De-data, most fundamentally using error-weighted approaches.

Usage

calc_Statistics(data, weight.calc = "square", digits = NULL, n.MCM = 1000, na.rm = TRUE)

Arguments

data

data.frame or RLum.Results object (required): for data.frame two columns: De (data[,1]) and De error (data[,2]). To plot several data sets in one plot the data sets must be provided as list, e.g. list(data.1, data.2).

weight.calc

character: type of weight calculation. One out of "reciprocal" (weight is 1/error), "square" (weight is 1/error^2). Default is "square".

digits

integer (with default): round numbers to the specified digits. If digits is set to NULL nothing is rounded.

n.MCM

numeric (with default): number of samples drawn for Monte Carlo-based statistics. Set to zero to disable this option.

na.rm

logical (with default): indicating whether NA values should be stripped before the computation proceeds.

Value

Returns a list with weighted and unweighted statistic measures.

Function version

0.1.6 (2016-05-16 22:14:31)

Details

The option to use Monte Carlo Methods (n.MCM > 0) 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.

Examples

## 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)

## 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)