Description Usage Arguments Details Value Author(s) References See Also Examples
hbm.features
computes the main features of a hierarchical block matrix.
1  hbm.features(m, noise.factor, ncores = 1, ref = NULL, ...)

m 
a numeric association matrix, typically a chromatin contact map. 
noise.factor 
numeric vector giving the noise factor to add with 
ncores 
integer giving the number of cores to register and use. If this is larger than one, iterations will be executed in parallel. 
ref 
hierarchical block matrix computed with 
... 
additional parameters for 
hbm.features
adds noise to the given association matrix and executes hbm
to generate a hierarchical block matrix. Repeating this for multiple iterations (with the same or different noise factor values) gives a mean hierarchical block matrix that can be compared with the matrix computed from the non noisy association matrix.
hbm.features
returns a list with the following objects:
noisy.hm 
The average hierarchical block matrix. A numeric matrix whose i,jth entry gives the mean scale at which i and j were found in the same cluster across 
features 

Yoli Shavit
hbm
's website: http://www.cl.cam.ac.uk/~ys388/hbm/
add.noise
to see how noise is added to matrices
hbm
to learn how to build hierarchical block matrices
hbm
's tutorials at http://www.cl.cam.ac.uk/~ys388/hbm/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  set.seed(2)
n = 100 # chain size
# generate chain configuration (random walk/giant loop model)
conf = generate.random.conf(n, sd = 0.5, scale = FALSE)
# generate a contact map like matrix using the model c ~ exp(d)
control = exp(1*as.matrix(dist(conf)))
noise = rep(10, 10)
res = hbm.features(control, noise, prune = TRUE, pruning.prob = 0.01)
m = res$features
image(t(m)[,nrow(m):1], axes = FALSE)
ats = seq(0,1,0.2)
lbls = as.character(n*ats)
axis(1, at= ats, labels = lbls, cex.axis = 0.8)
ats = seq(1,0,1*0.2)
lbls = as.character(n*seq(0,1,0.2))
axis(2, at= ats, labels = lbls, cex.axis = 0.8)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.