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.
A user-supplied descriptive name for the model.
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.
Number of levels of recursive subdivision. The number of bins in the model will equal 2^nRecursions.
Names of flowFrames from the FlowSet used to construct the model.
Names of all of the parameters from the flowFrames from the FlowSet used to construct the model.
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.
A hairy array, aka list of vectors. Each list element is a vector representing the median values at which the data were split.
A hairy array, aka list of vectors. Each list element is a vector representing the axis on which the data were split.
An object of class
binBoundary, used to hold boundary information
used primarily for visualization.
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.
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.)
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 (flowFrames) in training data. For example, it does not make sense to compare PE from one flowFrame with FITC from another.
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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