emnormalCNV {CNVrd2} | R Documentation |
This function is used to obtain the maximization likelihood estimation of normal mixture model by using the EM algorithm (Demster et al., 1977).
emnormalCNV(Object, ...)
Object |
An object of class clusteringCNVs. |
... |
Optional arguments |
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 |
z |
Data frame of proportions of data in mixture components. |
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).
Hoang Tan Nguyen, Tony R Merriman and MA Black. hoangtannguyenvn@gmail.com
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.
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)