mixglasso: mixglasso

In nethet: A bioconductor package for high-dimensional exploration of biological network heterogeneity

Description

mixglasso

Usage

mixglasso(x, n.comp, lambda = sqrt(2 * nrow(x) * log(ncol(x)))/2,
  pen = "glasso.parcor", init = "kmeans.hc", my.cl = NULL,
  modelname.hc = "VVV", nstart.kmeans = 1, iter.max.kmeans = 10,
  term = 10^{     -3 }, min.compsize = 5, save.allfits = FALSE,
  filename = "mixglasso_fit.rda", mc.flag = FALSE,
  mc.set.seed = FALSE, mc.preschedule = FALSE,
  mc.cores = getOption("mc.cores", 2L), ...)

Arguments

x	Input data matrix
n.comp	Number of mixture components. If n.comp is a vector, mixglasso will estimate a model for each number of mixture components, and return a list of models, as well as their BIC and MMDL scores and the index of the best model according to each score.
lambda	Regularization parameter. Default=sqrt(2*n*log(p))/2
pen	Determines form of penalty: glasso.parcor (default) to penalise the partial correlation matrix, glasso.invcov to penalise the inverse covariance matrix (this corresponds to classical graphical lasso), glasso.invcor to penalise the inverse correlation matrix.
init	Initialization. Method used for initialization init='cl.init','r.means','random','kmeans','kmeans.hc','hc'. Default='kmeans'
my.cl	Initial cluster assignments; need to be provided if init='cl.init' (otherwise this param is ignored). Default=NULL
modelname.hc	Model class used in hc. Default="VVV"
nstart.kmeans	Number of random starts in kmeans; default=1
iter.max.kmeans	Maximal number of iteration in kmeans; default=10
term	Termination criterion of EM algorithm. Default=10^-3
min.compsize	Stop EM if any(compsize)<min.compsize; Default=5
save.allfits	If TRUE, save output of mixglasso for all k's.
filename	If save.allfits is TRUE, output of mixglasso will be saved as paste(filename, _fit.mixgl_k.rda, sep='').
mc.flag	If TRUE use parallel execution for each n.comp via function mclapply of package parallel.
mc.set.seed	See mclapply. Default=FALSE
mc.preschedule	See mclapply. Default=FALSE
mc.cores	Number of cores to use in parallel execution. Defaults to mc.cores option if set, or 2 otherwise.
...	Other arguments. See mixglasso_init

Details

Runs mixture of graphical lasso network clustering with one or several numbers of mixture components.

Value

A list with elements:

models	List with each element i containing an S3 object of class 'nethetclustering' that contains the result of fitting the mixture graphical lasso model with n.comps[i] components. See the documentation of mixglasso_ncomp_fixed for the description of this object.
bic	BIC for all fits.
mmdl	Minimum description length score for all fits.
comp	Component assignments for all fits.
bix.opt	Index of model with optimal BIC score.
mmdl.opt	Index of model with optimal MMDL score.

Author(s)

n.stadler

Examples

###########################################
##This an example of how to use MixGLasso##
###########################################

##generate data
set.seed(1)
n <- 1000
n.comp <- 3
p <- 10

# Create different mean vectors
Mu <- matrix(0,p,n.comp)

nonzero.mean <- split(sample(1:p),rep(1:n.comp,length=p))
for(k in 1:n.comp){
  Mu[nonzero.mean[[k]],k] <- -2/sqrt(ceiling(p/n.comp))
}

sim <- sim_mix_networks(n, p, n.comp, Mu=Mu)

##run mixglasso
set.seed(1)
fit1 <-  mixglasso(sim$data,n.comp=1:6)
fit1$bic
set.seed(1)
fit2 <-  mixglasso(sim$data,n.comp=6)
fit2$bic
set.seed(1)
fit3 <-  mixglasso(sim$data,n.comp=1:6,lambda=0)
set.seed(1)
fit4 <-  mixglasso(sim$data,n.comp=1:6,lambda=Inf)
#set.seed(1)
#fit5 <-  bwprun_mixglasso(sim$data,n.comp=1,n.comp.max=5,selection.crit='bic')
#plot(fit5$selcrit,ylab='bic',xlab='Num.Comps',type='b')

##compare bic
library('ggplot2')
plotting.frame <- 
  data.frame(BIC= c(fit1$bic, fit3$bic, fit4$bic), 
             Num.Comps=rep(1:6, 3), 
             Lambda=rep(c('Default', 
                          'Lambda = 0',
                          'Lambda = Inf'),
                        each=6))

p <- ggplot(plotting.frame) + 
	geom_line(aes(x=Num.Comps, y=BIC, colour=Lambda))

print(p)

nethet documentation built on Nov. 8, 2020, 6:54 p.m.