Description Usage Arguments Value Note Author(s) See Also Examples
This function is intended to design low-level uses of SIMoNe by specifying various parameters of the underlying algorithms.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | setOptions(normalize = TRUE,
verbose = TRUE,
penalties = NULL,
penalty.min = NULL,
penalty.max = NULL,
n.penalties = 100,
edges.max = Inf,
edges.sym.rule = NULL,
edges.steady = "neighborhood.selection",
edges.coupling = "coopLasso",
clusters.crit = "BIC",
clusters.meth = "bayesian",
clusters.qmin = 2,
clusters.qmax = 4)
|
normalize |
logical specifying wether the data should be normalized to unit
variance. The normalization is made task-wisely in the multiple
sample setting. Default is |
verbose |
a logical that indicates verbose mode to display
progression. Default is |
penalties |
vector of decreasing penalty levels for the network
estimation. If |
penalty.min |
The minimal value of the penalty that will be tried for network
inference. If |
penalty.max |
The maximal value of the penalty that will be tried for network
inference. If |
n.penalties |
integer that indicates the number of penalties to put in the
|
edges.max |
integer giving an upper bound for the number of edges to select: if
a network is inferred along the algorithm with a number of edges
overstepping |
edges.steady |
a character string indicating the method to use for the network
inference associated to steady-state data, one task
framework. Either |
edges.coupling |
character string (either |
edges.sym.rule |
character string ( |
clusters.crit |
criterion to select the network that is used to find an underlying
clustering. Either |
clusters.qmin |
minimum number of classes for clustering. Default is 2. |
clusters.qmax |
maximum number of classes for clustering. Default is 4. |
clusters.meth |
character string indicating the strategy used for the estimation:
|
A list that contains all the specified parameters.
If the user specifies its own penalties
vector, all the
networks inferred during the algorithm will be kept, even if they
share the very same number of edges.
On the other hand, if you only specify penalty.max
and/or
penalty.min
and/or n.penalties
, the algorithm will only
kept the networks who show different numbers of edges. That is to say,
the number of networks stocked in the output of simone
generally does not have a length equal to n.penalties
.
J. Chiquet
1 2 | ## generate an object (list) with the default parameters
setOptions()
|
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