learn.dynamic.network {bnstruct}  R Documentation 
Learn a dynamic network (structure and parameters) of a BN from a BNDataset (see the Details
section).
This method is a wrapper for learn.network
to simplify the learning of a dynamic network.
It provides an automated generation of the layering
required to represent the set of time constraints
encoded in a dynamic network. In this function, it is assumed that the dataset contains the observations for each variable
in all the time steps:
V_1^{t_1}, V_2^{t_1}, V_n^{t_1}, V_1^{t_2}, ... , V_n^{t_k}
.
Variables in time step j
can be parents for any variable in time steps k>=j
, but not for variables i<j
.
If a layering is provided for a time step, it is valid in each time step, and not throughout the whole dynamic network;
a global layering can however be provided.
learn.dynamic.network(x, ...) ## S4 method for signature 'BN' learn.dynamic.network( x, y = NULL, num.time.steps = num.time.steps(y), algo = "mmhc", scoring.func = "BDeu", initial.network = NULL, alpha = 0.05, ess = 1, bootstrap = FALSE, layering = c(), max.fanin = num.variables(y)  1, max.fanin.layers = NULL, max.parents = num.variables(y)  1, max.parents.layers = NULL, layer.struct = NULL, cont.nodes = c(), use.imputed.data = FALSE, use.cpc = TRUE, mandatory.edges = NULL, ... ) ## S4 method for signature 'BNDataset' learn.dynamic.network( x, num.time.steps = num.time.steps(x), algo = "mmhc", scoring.func = "BDeu", initial.network = NULL, alpha = 0.05, ess = 1, bootstrap = FALSE, layering = c(), max.fanin = num.variables(x)  1, max.fanin.layers = NULL, max.parents = num.variables(x)  1, max.parents.layers = NULL, layer.struct = NULL, cont.nodes = c(), use.imputed.data = FALSE, use.cpc = TRUE, mandatory.edges = NULL, ... )
x 
can be a 
... 
potential further arguments for methods. 
y 

num.time.steps 
the number of time steps to be represented in the dynamic BN. 
algo 
the algorithm to use. Currently, one among 
scoring.func 
the scoring function to use. Currently, one among

initial.network 
network structure to be used as starting point for structure search.
Can take different values:
a 
alpha 
confidence threshold (only for 
ess 
Equivalent Sample Size value. 
bootstrap 

layering 
vector containing the layers each node belongs to. 
max.fanin 
maximum number of parents for each node (only for 
max.fanin.layers 
matrix of available parents in each layer (only for 
max.parents 
maximum number of parents for each node (for 
max.parents.layers 
matrix of available parents in each layer (only for 
layer.struct 

cont.nodes 
vector containing the index of continuous variables. 
use.imputed.data 

use.cpc 
(when using 
mandatory.edges 
binary matrix, where a 
The other parameters available are the ones of learn.network
, refer to the documentation of that function
for more details. See also the documentation for learn.structure
and learn.params
for more informations.
new BN
object with structure (DAG) and conditional probabilities
as learnt from the given dataset.
learn.network learn.structure learn.params
## Not run: mydataset < BNDataset("data.file", "header.file") net < learn.dynamic.network(mydataset, num.time.steps=2) ## End(Not run)