\HeaderA{density1d}{Find most likely separation between positive and negative
populations in 1D}{density1d}
\begin{Description}\relax
The function tries to find a reasonable split point between the two
hypothetical cell populations "positive" and "negative". This function
is considered internal, please use the API provided by
\code{\LinkA{rangeGate}{rangeGate}}.
\end{Description}
\begin{Usage}
\begin{verbatim}
density1d(x, stain, alpha = "min", sd = 2, plot = FALSE, borderQuant =
0.1, absolute = TRUE, inBetween = FALSE, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] A \code{\LinkA{flowSet}{flowSet}} or
\code{\LinkA{flowFrame}{flowFrame}}. 
\item[\code{stain}] A character scalar giving the flow parameter for which
to compute the separation. 
\item[\code{alpha}] A tuning parameter that controls the location of the
split point between the two populations. This has to be a numeric in
the range \code{[0,1]}, where values closer to 0 will shift the
split point closer to the negative population and values closer to 1
will shift towards the positive population. Additionally, the value
of \code{alpha} can be \code{"min"}, in which case the split point
will be selected as the area of lowest local density between the two
populations. 
\item[\code{sd}] For the case where there is only a single population, the
algorithm falls back to esitmating the mode of this population and a
robust measure of the variance of it distribution. The \code{sd}
tuning parameter controls how far away from the mode the split point
is set. 
\item[\code{plot}] Create a plot of the results of the computation. 
\item[\code{borderQuant}] Usualy the instrument is set up in a way that the
positive population is somewhere on the high end of the measurement
range and the negative population is on the low end. This parameter
allows to disregard populations with mean values in the extreme
quantiles of the data range. It's value should be in the range
\code{[0,1]}. 
\item[\code{absolute}] Logical controling whether to classify a population
(positive or negative) relative to the theoretical measurment range
of the instrument or the actual range of the data. This can be set
to \code{TRUE} if the alignment of the measurment range is not
optimal and the bulk of the data is on one end of the theoretical
range.
\item[\code{inBetween}] Force the algorithm to put the separator in between
two peaks. If there are more than two peaks, this argument is
ignored.
\item[\code{...}] Further arguments. 
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The algorithm first tries to identify high density regions in the
data. If the input is a \code{flowSet}, density regions will be
computed on the collapsed data, hence it should have been normalized
before (see \code{\LinkA{warpSet}{warpSet}} for one possible normalization
technique). The high density regions are then clasified as positive
and negative populations, based on their mean value in the theoretical
(or absolute if argument \code{absolute=TRUE}) measurement range. In
case there are only two high-density regions the lower one is usually
clasified as the negative populations, however the heuristics in the
algorithm will force the classification towards a positive population
if the mean value is already very high. The \code{absolute} and
\code{borderQuant} arguments can be used to control this
behaviour. The split point between populations will be drawn at the
value of mimimum local density between the two populations, or, if the
\code{alpha} argument is used, somewhere between the two populations
where the value of alpha forces the point to be closer to the negative
(\code{0 - 0.5}) or closer to the positive population (\code{0.5 -
  1}).

If there is only a single high-density region, the algorithm will fall
back to estimating the mode of the distribution
(\code{\LinkA{hubers}{hubers}}) and a robust measure of it's variance
and, in combination with the \code{sd} argument, set the split point
somewhere in the right or left tail, depending on the classification
of the region.

For more than two populations, the algorithm will still classify each
population into positive and negative and compute the split point
between those clusteres, similar to the two population case.
\end{Details}
\begin{Value}
A numeric indicating the split point between positive and negative populations.
\end{Value}
\begin{Author}\relax
Florian Hahne
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{warpSet}{warpSet}},  \code{\LinkA{rangeGate}{rangeGate}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}

data(GvHD)
dat <- GvHD[pData(GvHD)$Patient==10]
dat <- transform(dat, "FL4-H"=asinh(`FL4-H`), "FL3-H"=asinh(`FL3-H`))
d <- flowStats:::density1d(dat, "FL4-H", plot=TRUE)
if(require(flowViz))
densityplot(~`FL4-H`, dat, refline=d)

## tweaking the location
flowStats:::density1d(dat, "FL4-H", plot=TRUE, alpha=0.8)

## only a single population
flowStats:::density1d(dat, "FL3-H", plot=TRUE)
flowStats:::density1d(dat, "FL3-H", plot=TRUE, sd=2)

\end{ExampleCode}
\end{Examples}

