CNVrd2 (version 1.10.2)

emnormalCNV: 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.

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)