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')
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(nethet)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/nethet/bwprun_mixglasso.Rd_%03d_medium.png", width=480, height=480)
> ### Name: bwprun_mixglasso
> ### Title: bwprun_mixglasso
> ### Aliases: bwprun_mixglasso
>
> ### ** 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$data,n.comp=1,n.comp.max=5,selection.crit='bic')
Kmax= 5
bwprun: nr comps 5
merge or delete? delete comp 4
bwprun: nr comps 4
merge or delete? delete comp 1
bwprun: nr comps 3
merge or delete? merge comps 5 3
bwprun: nr comps 2
merge or delete? merge comps 5+3 2
bwprun: nr comps 1
> plot(fit$selcrit,ylab='bic',xlab='Num.Comps',type='b')
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.