nethet (version 1.4.2)

mixglasso: mixglasso

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)

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

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.

Details

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

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)