Last data update: 2014.03.03

 R: Fit and plot a growth curve for luminescence data (Lx/Tx...
plot_GrowthCurveR Documentation

Fit and plot a growth curve for luminescence data (Lx/Tx against dose)

Description

A dose response curve is produced for luminescence measurements using a regenerative protocol.

Usage

plot_GrowthCurve(sample, na.rm = TRUE, fit.method = "EXP",
  fit.force_through_origin = FALSE, fit.weights = TRUE,
  fit.includingRepeatedRegPoints = TRUE, fit.NumberRegPoints = NULL,
  fit.NumberRegPointsReal = NULL, fit.bounds = TRUE,
  NumberIterations.MC = 100, output.plot = TRUE,
  output.plotExtended = TRUE, output.plotExtended.single = FALSE,
  cex.global = 1, txtProgressBar = TRUE, verbose = TRUE, ...)

Arguments

sample

data.frame (required): data frame with three columns for x=Dose,y=LxTx,z=LxTx.Error, y1=TnTx. The column for the test dose response is optional, but requires 'TnTx' as column name if used. For exponential fits at least three dose points (including the natural) should be provided.

na.rm

logical (with default): excludes NA values from the data set prior to any further operations.

fit.method

character (with default): function used for fitting. Possible options are: LIN, QDR, EXP, EXP OR LIN, EXP+LIN or EXP+EXP. See details.

fit.force_through_origin

logical (with default) allow to force the fitted function through the origin. For method = "EXP+EXP" the function will go to the origin in either case, so this option will have no effect.

fit.weights

logical (with default): option whether the fitting is done with or without weights. See details.

fit.includingRepeatedRegPoints

logical (with default): includes repeated points for fitting (TRUE/FALSE).

fit.NumberRegPoints

integer (optional): set number of regeneration points manually. By default the number of all (!) regeneration points is used automatically.

fit.NumberRegPointsReal

integer (optional): if the number of regeneration points is provided manually, the value of the real, regeneration points = all points (repeated points) including reg 0, has to be inserted.

fit.bounds

logical (with default): set lower fit bounds for all fitting parameters to 0. Limited for the use with the fit methods EXP, EXP+LIN and EXP OR LIN. Argument to be inserted for experimental application only!

NumberIterations.MC

integer (with default): number of Monte Carlo simulations for error estimation. See details.

output.plot

logical (with default): plot output (TRUE/FALSE).

output.plotExtended

logical (with default): If TRUE, 3 plots on one plot area are provided: (1) growth curve, (2) histogram from Monte Carlo error simulation and (3) a test dose response plot. If FALSE, just the growth curve will be plotted. Requires: output.plot = TRUE.

output.plotExtended.single

logical (with default): single plot output (TRUE/FALSE) to allow for plotting the results in single plot windows. Requires output.plot = TRUE and output.plotExtended = TRUE.

cex.global

numeric (with default): global scaling factor.

txtProgressBar

logical (with default): enables or disables txtProgressBar. If verbose = FALSE also no txtProgressBar is shown.

verbose

logical (with default): enables or disables terminal feedback.

...

Further arguments and graphical parameters to be passed. Note: Standard arguments will only be passed to the growth curve plot. Supported: xlim, ylim, main, xlab, ylab

Details

Fitting methods

For all options (except for the LIN, QDR and the EXP OR LIN), the nlsLM function with the LM (Levenberg-Marquardt algorithm) algorithm is used. Note: For historical reasons for the Monte Carlo simulations partly the function nls using the port algorithm.

The solution is found by transforming the function or using uniroot.

LIN: fits a linear function to the data using lm:

y = m*x+n

QDR: fits a linear function to the data using lm:

y = a + b * x + c * x^2

EXP: try to fit a function of the form

y = a*(1-exp(-(x+c)/b))

Parameters b and c are approximated by a linear fit using lm. Note: b = D0

EXP OR LIN: works for some cases where an EXP fit fails. If the EXP fit fails, a LIN fit is done instead.

EXP+LIN: tries to fit an exponential plus linear function of the form:

y = a*(1-exp(-(x+c)/b)+(g*x))

The De is calculated by iteration.
Note: In the context of luminescence dating, this function has no physical meaning. Therefore, no D0 value is returned.

EXP+EXP: tries to fit a double exponential function of the form

y = (a1*(1-exp(-(x)/b1)))+(a2*(1-exp(-(x)/b2)))

This fitting procedure is not robust against wrong start parameters and should be further improved.

Fit weighting

If the option fit.weights = TRUE is chosen, weights are calculated using provided signal errors (Lx/Tx error):

fit.weights = 1/error/(sum(1/error))


Error estimation using Monte Carlo simulation

Error estimation is done using a Monte Carlo (MC) simulation approach. A set of Lx/Tx values is constructed by randomly drawing curve data from samled from normal distributions. The normal distribution is defined by the input values (mean = value, sd = value.error). Then, a growth curve fit is attempted for each dataset resulting in a new distribution of single De values. The sd of this distribution is becomes then the error of the De. With increasing iterations, the error value becomes more stable. Note: It may take some calculation time with increasing MC runs, especially for the composed functions (EXP+LIN and EXP+EXP).
Each error estimation is done with the function of the chosen fitting method.

Subtitle information

To avoid plotting the subtitle information, provide an empty user mtext mtext = "". To plot any other subtitle text, use mtext.

Value

Along with a plot (so far wanted) an RLum.Results object is returned containing, the slot data contains the following elements:

DATA.OBJECT TYPE DESCRIPTION
..$De : data.frame Table with De values
..$De.MC : numeric Table with De values from MC runs
..$Fit : nls or lm object from the fitting for EXP, EXP+LIN and EXP+EXP. In case of a resulting linear fit when using LIN, QDR or EXP OR LIN
..$Formula : expression Fitting formula as R expression
..$call : call The original function call

Function version

1.8.12 (2016-05-29 17:57:29)

Author(s)

Sebastian Kreutzer, IRAMAT-CRP2A, Universite Bordeaux Montaigne (France),
Michael Dietze, GFZ Potsdam (Germany)
R Luminescence Package Team

See Also

nls, RLum.Results, get_RLum, nlsLM, lm, uniroot

Examples


##(1) plot growth curve for a dummy data.set and show De value
data(ExampleData.LxTxData, envir = environment())
temp <- plot_GrowthCurve(LxTxData)
get_RLum(temp)

##(1a) to access the fitting value try
get_RLum(temp, data.object = "Fit")

##(2) plot the growth curve only - uncomment to use
##pdf(file = "~/Desktop/Growth_Curve_Dummy.pdf", paper = "special")
plot_GrowthCurve(LxTxData)
##dev.off()

##(3) plot growth curve with pdf output - uncomment to use, single output
##pdf(file = "~/Desktop/Growth_Curve_Dummy.pdf", paper = "special")
plot_GrowthCurve(LxTxData, output.plotExtended.single = TRUE)
##dev.off()

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.
Type 'contributors()' for more information and
'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]
The undecided OSL component: 'Should I stay or should I go?'
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Luminescence/plot_GrowthCurve.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot_GrowthCurve
> ### Title: Fit and plot a growth curve for luminescence data (Lx/Tx against
> ###   dose)
> ### Aliases: plot_GrowthCurve
> 
> ### ** Examples
> 
> 
> ##(1) plot growth curve for a dummy data.set and show De value
> data(ExampleData.LxTxData, envir = environment())
> temp <- plot_GrowthCurve(LxTxData)
[plot_GrowthCurve()] Fit: EXP | De = 1737.88 | D01 = 1766.07
> get_RLum(temp)
       De De.Error     D01 D01.ERROR D02 D02.ERROR  De.MC Fit
1 1737.88    53.35 1766.07  85.97893  NA        NA 1732.3 EXP
> 
> ##(1a) to access the fitting value try
> get_RLum(temp, data.object = "Fit")
Nonlinear regression model
  model: y ~ a * (1 - exp(-(x + c)/b))
   data: data
       a        b        c 
   6.806 1766.074    5.051 
 weighted residual sum-of-squares: 0.0004268

Number of iterations to convergence: 4 
Achieved convergence tolerance: 1.49e-08
> 
> ##(2) plot the growth curve only - uncomment to use
> ##pdf(file = "~/Desktop/Growth_Curve_Dummy.pdf", paper = "special")
> plot_GrowthCurve(LxTxData)
[plot_GrowthCurve()] Fit: EXP | De = 1737.88 | D01 = 1766.07
> ##dev.off()
> 
> ##(3) plot growth curve with pdf output - uncomment to use, single output
> ##pdf(file = "~/Desktop/Growth_Curve_Dummy.pdf", paper = "special")
> plot_GrowthCurve(LxTxData, output.plotExtended.single = TRUE)
[plot_GrowthCurve()] Fit: EXP | De = 1737.88 | D01 = 1766.07
> ##dev.off()
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and IMSBIO