Description Usage Arguments Details Value Note Author(s) See Also Examples
The simone
function offers an interface to infer networks based
on partial correlation coefficients in various contexts and methods
(steady-state data, time-course data, multiple sample setup,
clustering prior)
1 2 3 4 5 |
X |
a n x p matrix of data, typically n
expression levels associated to the same p genes. Can also
be a |
type |
a character string indicating the data specification (either
|
clustering |
a logical indicating if the network inference should be perfomed by
penalizing the edges according to a latent clustering discovered
during the network structure recovery. Default is |
tasks |
A factor with n entries indicating the task belonging
for each observation in the multiple sample framework. Default is
|
control |
A list that is used to specify low-level options for the
algorithm, defined through the |
Any inference method available ("neighborhood selection",
"graphical-Lasso", "VAR(1) inference" and "multitask learning" - see
simone-package
) relies on an optimization problem under
the general form
clustering
is set to TRUE
.
The model and the penalty function
penl1 differ according to the context
(steady-state/time-course data, multitask learning and its associated
coupling effect). For further details on the models, please check the
papers listed in the reference section of
simone-package
.
The criterion displayed during a SIMoNe run is the value of the penalized likelihood for the current values of the estimor Θhat(λ) corresponding to a given value of the overall penalty level λ.
The following information criteria are also computed for any value of
λ
and part of the output of simone
. The BIC (Bayesian
Information Criterion)
and the AIC (Akaike Information Criterion)
Returns an object of class simone
, which is list-like and
contains the following:
networks |
a list with all the inferred networks stocked as adjacency matrices (the successive values of
Θ
controled by the penalty level
λ). In
the multiple sample setup, each element of the list is a list with
as many entries as samples or levels in |
penalties |
a vector of the same length as |
n.edges |
a vector of the same length as |
BIC |
a vector of the same length as |
AIC |
a vector of the same length as |
clusters |
a size-p factor indicating the class of each variable. |
weights |
a pxp matrix of weigths used to adapt the penalty to
each entry of the |
control |
a list describing all the posterior values of the parameters used by
the algorithm, to compare with the one set by the
|
If nothing particular is specified about the penalty through the
control
list (see setOptions
), the default is to
start from a value of
λ
that ensures an empty network. Then
λ is
progressively shrinked, as close to zero as possible. Along the
shrinkage of
λ,
only networks with different numbers of edges are kept in the final
output.
J. Chiquet
setOptions
, plot.simone
,
cancer
and demo(package="simone")
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ## load the breast cancer data set
data(cancer)
attach(cancer)
## launch simone with the default parameters and plot results
plot(simone(expr))
## Not run:
## try with clustering now (clustering is achieved on a 30-edges network)
plot(simone(expr, clustering=TRUE, control=setOptions(clusters.crit=30)))
## try the multiple sample
plot(simone(expr, tasks=status))
## End(Not run)
detach(cancer)
|