bwprun_mixglasso: bwprun_mixglasso

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

Description

Mixglasso with backward pruning

Usage

bwprun_mixglasso(x, n.comp.min = 1, n.comp.max, lambda = sqrt(2 *
  nrow(x) * log(ncol(x)))/2, pen = "glasso.parcor",
  selection.crit = "mmdl", term = 10^{     -3 }, min.compsize = 5,
  init = "kmeans.hc", my.cl = NULL, modelname.hc = "VVV",
  nstart.kmeans = 1, iter.max.kmeans = 10, reinit.out = FALSE,
  reinit.in = FALSE, mer = TRUE, del = TRUE, ...)

Arguments

x	Input data matrix
n.comp.min	Minimum number of components. Take n.comp.min=1 !
n.comp.max	Maximum number of components
lambda	Regularization parameter. Default=sqrt(2*n*log(p))/2
pen	Determines form of penalty: glasso.parcor (default), glasso.invcov, glasso.invcor
selection.crit	Selection criterion. Default='mmdl'
term	Termination criterion of EM algorithm. Default=10^-3
min.compsize	Stop EM if any(compsize)<min.compsize; Default=5
init	Initialization. Method used for initialization init='cl.init','r.means','random','kmeans','kmeans.hc','hc'. Default='kmeans.hc'
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
reinit.out	Re-initialization if compsize<min.compsize (at the start of algorithm) ?
reinit.in	Re-initialization if compsize<min.compsize (at the bwprun-loop level of algorithm) ?
mer	Merge closest comps for initialization
del	Delete smallest comp for initialization
...	Other arguments. See mixglasso_init

Details

This function runs mixglasso with various number of mixture components: It starts with a too large number of components and iterates towards solutions with smaller number of components by initializing using previous solutions.

Value

list consisting of

selcrit	Selcrit for all models with number of components between n.comp.min and n.comp.max
res.init	Initialization for all components
comp.name	List of names of components. Indicates which states where merged/deleted during backward pruning
re.init.in	Logical vector indicating whether re-initialization was performed or not
fit.mixgl.selcrit	Results for model with optimal number of components. List see mixglasso_init

Author(s)

n.stadler

Examples

##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

fit <-  bwprun_mixglasso(sim$data,n.comp=1,n.comp.max=5,selection.crit='bic')
plot(fit$selcrit,ylab='bic',xlab='Num.Comps',type='b')

FrankD/nethet documentation built on Oct. 5, 2020, 8:22 a.m.