cell.FitModel {immunoClust} | R Documentation

## immunoClust EMt-iteration on Cell-events given initial Model Parameters

### Description

The function fits initial model parameters to specific observed cell event data. The function returns the cluster information of the fitted model in an object of class `immunoClust`.

### Usage

    cell.FitModel(x, data, B=50, tol=1e-5, bias=0.5, modelName="mvt" )

    cell.Classify(x, data, modelName="mvt" )

### Arguments

`x` | An immunoClust object with the initial model parameter (parameters, K, w, mu, sigma).
`data` | A numeric matrix, data frame of observations, or object of class flowFrame.
`B` | The maximum number of EMt-iterations.
`tol` | The tolerance used to assess the convergence of the EMt-algorithms.
`bias` | The ICL-bias used in the EMt-algorithm.
`modelName` | Used mixture model; either `"mvt"` or `"mvn"` for a t- or Gaussian mixture model respectively.

### Details

These functions are wrapper of the functions `cell.EM` and `cell.Estimation`, when model cluster parameters are combined in an object of class `immunoClust` and are used in the iterative cell event clustering process `cell.process` of immunoClust and are not intended to be called directly.

### Value

The fitted model cluster information in an object of class `immunoClust`.

### Author(s)

Till Sörensen till-antoni.soerensen@charite.de

### References

Sörensen, T., Baumgart, S., Durek, P., Grützkau, A. and Häupl, T. immunoClust - an automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. Cytometry A (accepted).

### See Also

`cell.process`, `cell.EM`, `cell.Estimation`

### Examples

    data(dat.fcs)
    data(dat.exp)
    r1 <- dat.exp[[1]]
    dat.trans <- trans.ApplyToData(r1, dat.fcs)
    r2 <- cell.FitModel(r1, dat.trans)

[Package immunoClust version 1.19.4 Index]