flowFP (version 1.30.0)

flowFPModel-class: Fingerprint model class description.

Description

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 from the Class

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

Slots

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.)

Methods

show

shows the contents of the model.

References

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.

See Also

flowFPModel - Constructor.

Examples

	# load a flowSet to use as an example.
	library(flowFP)
	data(fs1)
	mod = flowFPModel(fs1)