Last data update: 2014.03.03

R: Function to calculate statistic measures
 calc_Statistics R Documentation

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.

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.

Value

Returns a list with weighted and unweighted statistic measures.

Function version

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

Author(s)

Michael Dietze, GFZ Potsdam (Germany)
R Luminescence Package Team

Examples

```
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)

```

Results

```
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(Luminescence)
Welcome to the R package Luminescence version 0.6.0 [Built: 2016-05-30 16:47:30 UTC]
A Windows user: 'An apple a day keeps the doctor away.'
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Luminescence/calc_Statistics.Rd_%03d_medium.png", width=480, height=480)
> ### Name: calc_Statistics
> ### Title: Function to calculate statistic measures
> ### Aliases: calc_Statistics
> ### Keywords: datagen
>
> ### ** Examples
>
>
> 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))
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 2950
..\$ median  : num 2887
..\$ sd.abs  : num 294
..\$ sd.rel  : num 9.98
..\$ se.abs  : num 58.9
..\$ se.rel  : num 2
..\$ skewness: num 1289
..\$ kurtosis: num 4774
>
> ## Not run:
> ##D ## now the same for 10000 normal distributed random numbers with equal errors
> ##D x <- as.data.frame(cbind(rnorm(n = 10^5, mean = 0, sd = 1),
> ##D                          rep(0.001, 10^5)))
> ##D
> ##D ## note the congruent results for weighted and unweighted measures
> ##D str(calc_Statistics(x))
> ## End(Not run)
>
>
>
>
>
>
> dev.off()
null device
1
>

```