Description Objects from the Class Slots Methods Note Author(s) References See Also Examples

This class is the fundamental clase for the package. It contains data and methods used to construct a model of the probability density function of a prototype dataset provided in the form of a flowFrame or flowSet.

Objects may only be created by calling the constructor function flowFPModel.

`name`

:A user-supplied descriptive name for the model.

`parameters`

:List of FCS parameters to use for model creation. Can be specified either by the names of the parameters or the indices of the parameters.

`nRecursions`

:Number of levels of recursive subdivision. The number of bins in the model will equal

*2^nRecursions*.`trainingSet`

:Names of

*flowFrames*from the*FlowSet*used to construct the model.`trainingSetParams`

:Names of all of the parameters from the

*flowFrames*from the*FlowSet*used to construct the model.`dequantize`

:If TRUE, all of the event parameter values in the training set will be made unique by adding a tiny value (proportional to the ordinal position of each event) to the data.

`split_val`

:A hairy array, aka list of vectors. Each list element is a vector representing the median values at which the data were split.

`split_axis`

:A hairy array, aka list of vectors. Each list element is a vector representing the axis on which the data were split.

`binBoundary`

:An object of class

`binBoundary`

, used to hold boundary information used primarily for visualization.`.cRecursions`

:Private value to hold the number of levels of recursion used to construct this model. Using

`nRecursions`

the resolution of a fingerprint can be reduced, but it can never exceed this value.`.tmp_tags`

:Scratch array, total number of events in the training set long, that keeps track of the event's bin number. (this exists only to provide the underlying C function with a persistent scratch space it needs for bookkeeping. Not useful to the user.)

- show
shows the contents of the model.

When creating a model you must keep in mind that it doesn't make sense to create more bins
(which is *2^nRecursions*) than the total number of events used to create the model.
The constructor checks for this.

When creating a model, you should specify only parameters that are common to all of the instances
(*flowFrame*s) in training data. For example, it does not make sense to compare PE from one
*flowFrame* with FITC from another.

Herb Holyst <holyst@mail.med.upenn.edu>, Wade Rogers <rogersw@mail.med.upenn.edu>

M. Roederer, et. al. (2001) Probability Binning Comparison: A Metric for Quantitating
Multivariate Distribution Differences, *Cytometry* **45**, 47-55.

W. Rogers et. al. (2008) Cytometric Fingerprinting: Quantitative Characterization of
Multivariate Distributions, *Cytometry Part A* **73**, 430-441.

flowFPModel - Constructor.

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