emnormalCNV {CNVrd2} | R Documentation

## Implement the EM algorithm

### Description

This function is used to obtain the maximization likelihood estimation of normal mixture model by using the EM algorithm (Demster et al., 1977).

### Usage

    emnormalCNV(Object, ...)

### Arguments

`Object` | An object of class clusteringCNVs.
`...` | Optional arguments

### Value

`loglk` | Value of the likelihood function.
`p` | Proportions of groups.
`m` | Means of groups.
`sigma` | Standard deviations of groups.
`count` | A number of iteration to obtain convergence stage.
`bic` | See `searchGroupCNVs`.
`z` | Data frame of proportions of data in mixture components.

### Note

In the package, the distance between two initial means of the two nearest neighbor groups was set `groupDistance` = 0.25 as a default value to obtain initial values (using the `kmeans` function in R).

### Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. hoangtannguyenvn@gmail.com

### References

Dempster, A. P., Laird, N. M., Rubin, D. B., 1977. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 1-38.

### See Also

`searchGroupCNVs`, `groupCNVs`

### Examples

    data(fcgr3bMXL)

    sS <- resultSegment$segmentationScores
    #########Histogram###########################
    ###View segmentation scores##################
    hist(sS[, 1], 100)
    ############################################
    ##Number of components#######################
    ###Make an object of clusteringCNVs class######
    objectCluster <- new("clusteringCNVs",
                         x = sS[, 1], k = 4, EV = TRUE)

    set.seed(123)
    copynumberGroups <- groupCNVs(Object = objectCluster)

[Package CNVrd2 version 1.24.0 Index]