% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CW2pLMi.R
\name{CW2pLMi}
\alias{CW2pLMi}
\title{Transform a CW-OSL curve into a pLM-OSL curve via interpolation under linear
modulation conditions}
\usage{
CW2pLMi(values, P)
}
\arguments{
\item{values}{\linkS4class{RLum.Data.Curve} or \link{data.frame} (\strong{required}):
\linkS4class{RLum.Data.Curve} or \code{data.frame} with measured curve data of type
stimulation time (t) (\code{values[,1]}) and measured counts (cts) (\code{values[,2]})}

\item{P}{\link{vector} (\emph{optional}):
stimulation time in seconds. If no value is given the optimal value is
estimated automatically (see details). Greater values of P produce more
points in the rising tail of the curve.}
}
\value{
The function returns the same data type as the input data type with
the transformed curve values.

\strong{\code{RLum.Data.Curve}}

\tabular{rl}{
\verb{$CW2pLMi.x.t} \tab: transformed time values \cr
\verb{$CW2pLMi.method} \tab: used method for the production of the new data points
}
}
\description{
Transforms a conventionally measured continuous-wave (CW) OSL-curve into a
pseudo linearly modulated (pLM) curve under linear modulation conditions
using the interpolation procedure described by Bos & Wallinga (2012).
}
\details{
The complete procedure of the transformation is given in Bos & Wallinga
(2012). The input \code{data.frame} consists of two columns: time (t) and
count values (CW(t))

\strong{Nomenclature}
\itemize{
\item P = stimulation time (s)
\item 1/P = stimulation rate (1/s)
}

\strong{Internal transformation steps}

(1)
log(CW-OSL) values

(2)
Calculate t' which is the transformed time:
\deqn{t' = 1/2*1/P*t^2}

(3)
Interpolate CW(t'), i.e. use the log(CW(t)) to obtain the count values
for the transformed time (t'). Values beyond \code{min(t)} and \code{max(t)}
produce \code{NA} values.

(4)
Select all values for t' < \code{min(t)}, i.e. values beyond the time resolution
of t. Select the first two values of the transformed data set which contain
no \code{NA} values and use these values for a linear fit using \link{lm}.

(5)
Extrapolate values for t' < \code{min(t)} based on the previously obtained
fit parameters.

(6)
Transform values using
\deqn{pLM(t) = t/P*CW(t')}

(7)
Combine values and truncate all values for t' > \code{max(t)}

\strong{NOTE:}
The number of values for t' < \code{min(t)} depends on the stimulation
period (P) and therefore on the stimulation rate 1/P. To avoid the
production of too many artificial data at the raising tail of the determined
pLM curves it is recommended to use the automatic estimation routine for
\code{P}, i.e. provide no own value for \code{P}.
}
\note{
According to Bos & Wallinga (2012) the number of extrapolated points
should be limited to avoid artificial intensity data. If \code{P} is
provided manually and more than two points are extrapolated, a warning
message is returned.
}
\section{Function version}{
 0.3.1
}

\examples{

##(1)
##load CW-OSL curve data
data(ExampleData.CW_OSL_Curve, envir = environment())

##transform values
values.transformed <- CW2pLMi(ExampleData.CW_OSL_Curve)

##plot
plot(values.transformed$x, values.transformed$y.t, log = "x")

##(2) - produce Fig. 4 from Bos & Wallinga (2012)
##load data
data(ExampleData.CW_OSL_Curve, envir = environment())
values <- CW_Curve.BosWallinga2012

##open plot area
plot(NA, NA,
     xlim = c(0.001,10),
     ylim = c(0,8000),
     ylab = "pseudo OSL (cts/0.01 s)",
     xlab = "t [s]",
     log = "x",
     main = "Fig. 4 - Bos & Wallinga (2012)")


values.t <- CW2pLMi(values, P = 1/20)
lines(values[1:length(values.t[,1]),1],CW2pLMi(values, P = 1/20)[,2],
      col = "red", lwd = 1.3)
text(0.03,4500,"LM", col = "red", cex = .8)

values.t <- CW2pHMi(values, delta = 40)
lines(values[1:length(values.t[,1]),1],CW2pHMi(values, delta = 40)[,2],
      col = "black", lwd = 1.3)
text(0.005,3000,"HM", cex =.8)

values.t <- CW2pPMi(values, P = 1/10)
lines(values[1:length(values.t[,1]),1], CW2pPMi(values, P = 1/10)[,2],
      col = "blue", lwd = 1.3)
text(0.5,6500,"PM", col = "blue", cex = .8)

} 

\section{How to cite}{
Kreutzer, S., 2023. CW2pLMi(): Transform a CW-OSL curve into a pLM-OSL curve via interpolation under linear modulation conditions. Function version 0.3.1. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., 2023. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.23. https://CRAN.R-project.org/package=Luminescence
}

\references{
Bos, A.J.J. & Wallinga, J., 2012. How to visualize quartz OSL
signal components. Radiation Measurements, 47, 752-758.

\strong{Further Reading}

Bulur, E., 1996. An Alternative Technique For
Optically Stimulated Luminescence (OSL) Experiment. Radiation Measurements,
26, 701-709.

Bulur, E., 2000. A simple transformation for converting CW-OSL curves to
LM-OSL curves. Radiation Measurements, 32, 141-145.
}
\seealso{
\link{CW2pLM}, \link{CW2pHMi}, \link{CW2pPMi}, \link{fit_LMCurve},
\linkS4class{RLum.Data.Curve}
}
\author{
Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)

Based on comments and suggestions from:\cr
Adrie J.J. Bos, Delft University of Technology, The Netherlands
, RLum Developer Team}
\keyword{manip}
