From 94f1be09028cd207a690ee281230154f0a32502b Mon Sep 17 00:00:00 2001 From: RLumSK Date: Sun, 5 Feb 2017 16:06:10 +0100 Subject: [PATCH] Update release NEWS @tzerk --- NEWS.Rmd | 23 +++++++++++++++++------ NEWS.html | 55 +++++++++++++++++++++++++++++++++++++++++++------------ 2 files changed, 60 insertions(+), 18 deletions(-) diff --git a/NEWS.Rmd b/NEWS.Rmd index e5aecf8a4..a596bdc76 100644 --- a/NEWS.Rmd +++ b/NEWS.Rmd @@ -25,12 +25,12 @@ This release comes with two (one of them long requested) new analysis functions: #### Analyse fading measurements Calculating fading corrected ages (`calc_FadingCorr()`) following the approach by Huntley and Lamothe (2001) -was already possible in the package `'Luminescence'` since the 2012. However, a package internal -analysis of the fading measurements was still missing, until today. The new function `analyse_FadingMeasurement() -allows to analyse typical SAR measurement data. The function can be fed with an `RLum.Analysis` -object (raw measurement data) or with an $L_x/T_x$ table and returns various numerical -and graphical output. On the top: the returned output object can be directly forwared -to other functions, e.g., `calc_FadingCorr()` or `calc_Kars2008()`. +was already possible in the package `'Luminescence'` since 2012. However, a package internal +analysis of the fading measurements was still missing, until today. The new function `analyse_FadingMeasurement()` +closes this gap by enabling the analysis of typical 'SAR fading' measurement data. +The function can be directly fed with an `RLum.Analysis` object (raw measurement data) or with an $L_x/T_x$ table +and returns various numerical and graphical output. On the top: to output can be directly forwared +to other functions, e.g., `calc_FadingCorr()` or `calc_Kars2008()` for further calculations. ```{r, echo = FALSE, warning = FALSE, message=FALSE} library("Luminescence") @@ -70,7 +70,16 @@ g_value <- analyse_FadingMeasurement( #### Average Dose Model (Guérin et al., 2017) +To overcome the drawbacks of commonly used age (dose) models, Guérin et al., 2017 introduced +a new dose model that calculates the average dose and their extrinsic dispersion and standard error +by bootstrapping. The function fits neatly into the collection of functions dealing with +age (dose) models. Example for the package example data set: +```{r, fig.width=7, fig.height=3} +data(ExampleData.DeValues, envir = environment()) +AD <- calc_AverageDose(ExampleData.DeValues$CA1[1:56,], +sigma_m = 0.1) +``` ### Conversions @@ -82,6 +91,7 @@ conversion from proprietary input formats to CSV-files * `convert_Daybreak2CSV()` * `convert_PSL2CSV()` * `convert_XSYG2CSV()` +* `write_RLum2CSV()` ### Miscellaneous @@ -102,6 +112,7 @@ In order to further support this opperator, the package `magrittr` is now load b attaching the package `'Luminescence'` #### Other enhancements + * Thanks to Antoine Zink the function `read_Daybreak2R()` now handles also binary files produced by the software TLAPLLIC v.3.2, which is used for a Daybreak, model 1100. diff --git a/NEWS.html b/NEWS.html index 40aa99540..cb77eed2f 100644 --- a/NEWS.html +++ b/NEWS.html @@ -12,7 +12,7 @@ - + R Luminescence version 0.7.0 - Advanced Fish - @@ -70,7 +70,7 @@

R Luminescence version 0.7.0
- Advanced Fish -

R Luminescence Team

-

2017-02-04

+

2017-02-05

@@ -85,14 +85,14 @@

New analysis functions

This release comes with two (one of them long requested) new analysis functions:

Analyse fading measurements

-

Calculating fading corrected ages (calc_FadingCorr()) following the approach by Huntley and Lamothe (2001) was already possible in the package 'Luminescence' since the 2012. However, a package internal analysis of the fading measurements was still missing, until today. The new function analyse_FadingMeasurement() allows to analyse typical SAR measurement data. The function can be fed with anRLum.Analysisobject (raw measurement data) or with an $L_x/T_x$ table and returns various numerical and graphical output. On the top: the returned output object can be directly forwared to other functions, e.g.,calc_FadingCorr()orcalc_Kars2008()`.

+

Calculating fading corrected ages (calc_FadingCorr()) following the approach by Huntley and Lamothe (2001) was already possible in the package 'Luminescence' since 2012. However, a package internal analysis of the fading measurements was still missing, until today. The new function analyse_FadingMeasurement() closes this gap by enabling the analysis of typical ‘SAR fading’ measurement data. The function can be directly fed with an RLum.Analysis object (raw measurement data) or with an \(L_x/T_x\) table and returns various numerical and graphical output. On the top: to output can be directly forwared to other functions, e.g., calc_FadingCorr() or calc_Kars2008() for further calculations.

g_value <- analyse_FadingMeasurement(
  object = fading_data,
  structure = c("Lx", "Tx"),
  plot = TRUE,
  verbose = TRUE,
  n.MC = 100)
-

+

## 
 ## [analyse_FadingMeasurement()]
 ## 
@@ -101,11 +101,11 @@ 
Analyse fading measurements

 ## ---------------------------------------------------
 ## T_0.5 interpolated:  NA
 ## T_0.5 predicted: 4e+11
-## g-value:     5.18 ± 0.75 (%/decade)
-## g-value (norm. 2 days):  6.01 ± 0.77 (%/decade)
+## g-value:     5.18 ± 0.66 (%/decade)
+## g-value (norm. 2 days):  6.01 ± 0.67 (%/decade)
 ## ---------------------------------------------------
-## rho':            3.83e-06 ± 5.68e-07
-## log10(rho'):     -5.42 ± 0.06
+## rho':            3.95e-06 ± 6.73e-07
+## log10(rho'):     -5.4 ± 0.07
 ## ---------------------------------------------------
@@ -121,6 +121,35 @@

Kars et al., 2008

Average Dose Model (Guérin et al., 2017)

+

To overcome the drawbacks of commonly used age (dose) models, Guérin et al., 2017 introduced a new dose model that calculates the average dose and their extrinsic dispersion and standard error by bootstrapping. The function fits neatly into the collection of functions dealing with age (dose) models. Example for the package example data set:

+
data(ExampleData.DeValues, envir = environment())
+AD <- calc_AverageDose(ExampleData.DeValues$CA1[1:56,],
+sigma_m = 0.1)
+
## 
+## [calc_AverageDose()]
+## 
+## >> Initialisation <<
+## n:        56
+## delta:        65.7939285714286
+## sigma_m:  0.1
+## sigma_d:  0.286159381384861
+## 
+## >> Calculation <<
+## log likelihood:   -19.251
+## confidence intervals
+## --------------------------------------------------
+##                          IC_delta      IC_sigma_d
+## level                        0.95          0.9500
+## CredibleIntervalInf         61.06          0.2119
+## CredibleIntervalSup         70.20          0.4011
+## --------------------------------------------------
+## 
+## >> Results <<
+## ----------------------------------------------------------
+## Average dose:      65.3597   se(Aver. dose):  2.4181
+## sigma_d:   0.3092    se(sigma_d):     0.0482
+## ----------------------------------------------------------
+

@@ -131,6 +160,7 @@

Conversions

  • convert_Daybreak2CSV()
  • convert_PSL2CSV()
  • convert_XSYG2CSV()
    • +
  • write_RLum2CSV()
    • @@ -143,14 +173,15 @@

    Getting closer

    Pipe your data

    - +

    In the R-community a new operator, called ‘magrittr forward-pipe operator’ or short %>% from the package magrittr turned out as being very efficient for scripting R code. With this operator values can be direclty piped from one function to another. Example:

    +
    rnorm(1000) %T>% hist(freq = FALSE, breaks = "FD") %>% density %>% lines
    +

    +

    In order to further support this opperator, the package magrittr is now load by default when attaching the package 'Luminescence'

    Other enhancements