nopenalty: Clustering without penalty term

In FeiQin92/FLCNV: Simultaneous CNV Detection And Subclone Clustering With Single Cell Sequencing Data

Clustering without penalty term

Description

This function estimates parameters under the framework of classical mixture models without penalty term.

Usage

nopenalty(
  K,
  y,
  N = 100,
  kms.iter = 100,
  kms.nstart = 100,
  eps.diff = 1e-05,
  eps.em = 1e-05,
  model.crit = "gic"
)

Arguments

K	A vector of the number of clusters.
y	A p-dimensional data matrix. Each row is an observation.
N	The maximum number of iterations in the EM algorithm. The default value is 100.
kms.iter	The maximum number of iterations in the K-means algorithm whose outputs are the starting values for the EM algorithm.
kms.nstart	The number of starting values in K-means.
eps.diff	The lower bound of pairwise difference of two mean values. Any value lower than it is treated as 0.
eps.em	The lower bound for the stopping criterion.
model.crit	The criterion used to select the number of clusters K. It is either ‘bic’ for Bayesian Information Criterion or ‘gic’ for Generalized Information Criterion.

Details

This function estimates parameters \mu, \Sigma, \pi and the clustering assignments in the model with penalty term,

y \sim \sum_{k=1}^K \pi_k f(y|\mu_k, \Sigma)

where f(y|\mu_k, \Sigma_k) is the density function of Normal distribution with mean \mu_k and variance \Sigma. Here we assume that each cluster has the same diagonal variance.

Value

This function returns the esimated parameters and some statistics of the optimal model within the given K and \lambda, which is selected by BIC when model.crit = 'bic' or GIC when model.crit = 'gic'.

mu.hat.best	The estimated cluster means.
sigma.hat.best	The estimated covariance.
p.hat.best	The estimated cluster proportions.
s.hat.best	The clustering assignments.
K.best	The value of K that provides the optimal model.
llh.best	The log-likelihood of the optimal model.
gic.best	The GIC of the optimal model.
bic.best	The BIC of the optimal model.
ct.mu.best	The degrees of freedom in the cluster means of the optimal model.

References

Fraley, C., & Raftery, A. E. (2002) Model-based clustering, discriminant analysis, and density estimation. Journal of the American statistical Association 97(458), 611–631.

FeiQin92/FLCNV documentation built on June 13, 2025, 3:30 a.m.