norm2Filter-class.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/autoGate.R
\docType{class}
\name{norm2Filter-class}
\alias{norm2Filter-class}
\alias{norm2Filter}
\alias{show,norm2Filter-method}
\alias{\%in\%,flowFrame,norm2Filter-method}
\title{Class "norm2Filter"}
\usage{
norm2Filter(x, y, method="covMcd", scale.factor=1, n=50000,
filterId="defaultNorm2Filter")
}
\arguments{
\item{x, y}{Characters giving the names of the measurement parameter on which
the filter is supposed to work on. \code{y} can be missing in which case
\code{x} is expected to be a character vector of length 2 or a list of
characters.}

\item{filterId}{An optional parameter that sets the \code{filterId} slot of
this filter. The object can later be identified by this name.}

\item{scale.factor, n}{Numerics of length 1, used to set the
\code{scale.factor} and n slots of the object.}

\item{method}{Character in \code{covMcd} or \code{cov.rob}, used to set the
\code{method} slot of the object.}
}
\value{
Returns a \code{\link{norm2Filter}} object for use in filtering
\code{\link{flowFrame}}s or other flow cytometry objects.
}
\description{
Class and constructors for a \code{\link{filter}} that fits a bivariate
normal distribution to a data set of paired values and selects data points
according to their standard deviation from the fitted distribution.
}
\details{
The filter fits a bivariate normal distribution to the data and selects all
events within the Mahalanobis distance multiplied by the \code{scale.factor}
argument. The constructor \code{norm2Filter} is a convenience function for
object instantiation. Evaluating a \code{curv2Filter} results in an object
of class \code{\link{logicalFilterResult}}. Accordingly, \code{norm2Filters}
can be used to subset and to split flow cytometry data sets.
}
\section{Slots}{

\describe{
\item{\code{method}}{One of \code{covMcd} or \code{cov.rob}
defining method used for computation of covariance matrix.}

\item{\code{scale.factor}}{Numeric vector giving factor of standard
deviations used for data selection (all points within
\code{scalefac} standard deviations are selected).}

\item{\code{n}}{Object of class \code{"numeric"}, the number of
events used to compute the covariance matrix of the bivariate
distribution.}

\item{\code{filterId}}{Object of class \code{"character"}
referencing the filter.}

\item{\code{parameters}}{Object of class \code{"ANY"} describing
the parameters used to filter the \code{\link{flowFrame}} or
\code{\link{flowSet}}.}
}}

\note{
See the documentation in the \code{\link[flowViz:flowViz-package]{flowViz}}
package for plotting of \code{norm2Filters}.
}
\section{Extends}{


Class \code{"\linkS4class{parameterFilter}"}, directly.

Class \code{"\linkS4class{concreteFilter}"}, by class
\code{parameterFilter}, distance 2.

Class \code{"\linkS4class{filter}"}, by class \code{parameterFilter},
distance 3.
}

\section{Objects from the Class}{

  Objects can be created by calls of the form \code{new("norm2Filter",
...)} or using the constructor \code{norm2Filter}. The constructor
is the recommended way.
}

\section{Methods}{

\describe{
  
  \item{\%in\%}{\code{signature(x = "flowFrame", table =
                                  "norm2Filter")}: The workhorse used to evaluate the filter on
    data. This is usually not called directly by the user, but
    internally by calls to the \code{\link{filter}} methods. }
  
  \item{show}{\code{signature(object = "norm2Filter")}: Print
    information about the filter. }
  
}
}

\examples{

library(flowCore)
## Loading example data
dat <- read.FCS(system.file("extdata","0877408774.B08",
package="flowCore"))

## Create directly. Most likely from a command line
norm2Filter("FSC-H", "SSC-H", filterId="myCurv2Filter")

## To facilitate programmatic construction we also have the following
n2f <- norm2Filter(filterId="myNorm2Filter", x=list("FSC-H", "SSC-H"),
scale.factor=2)
n2f <- norm2Filter(filterId="myNorm2Filter", x=c("FSC-H", "SSC-H"),
scale.factor=2)

## Filtering using norm2Filter
fres <- filter(dat, n2f)
fres
summary(fres)

## The result of norm2 filtering is a logical subset
Subset(dat, fres)

## We can also split, in which case we get those events in and those
## not in the gate as separate populations
split(dat, fres)


}
\seealso{
\code{\link[MASS]{cov.rob}}, \code{\link[rrcov]{CovMcd}},
\code{\link[flowCore:filter-methods]{filter}} for evaluation of
\code{norm2Filters} and \code{\link{split}} and \code{\link{Subset}}for
splitting and subsetting of flow cytometry data sets based on that.
}
\author{
F. Hahne
}
\keyword{classes}
\keyword{methods}