scoreparameters {BiDAG}  R Documentation 
This function returns an object of class scoreparameters containing the data and parameters needed for calculation of the BDe/BGe score, or a user defined score.
scoreparameters( scoretype = c("bge", "bde", "bdecat", "usr"), data, bgepar = list(am = 1, aw = NULL), bdepar = list(chi = 0.5, edgepf = 2), bdecatpar = list(chi = 0.5, edgepf = 2), dbnpar = list(samestruct = TRUE, slices = 2, b = 0, stationary = TRUE, rowids = NULL, datalist = NULL), usrpar = list(pctesttype = c("bge", "bde", "bdecat")), mixedpar = list(nbin = 0), MDAG = FALSE, DBN = FALSE, weightvector = NULL, bgnodes = NULL, edgepmat = NULL, nodeslabels = NULL ) ## S3 method for class 'scoreparameters' print(x, ...) ## S3 method for class 'scoreparameters' summary(object, ...)
scoretype 
the score to be used to assess the DAG structure: "bge" for Gaussian data, "bde" for binary data, "bdecat" for categorical data, "usr" for a user defined score; when "usr" score is chosen, one must define a function (which evaluates the log score of a node given its parents) in the following format: usrDAGcorescore(j,parentnodes,n,param), where 'j' is node to be scores, 'parentnodes' are the parents of this node, 'n' number of nodes in the netwrok and 'param' is an object of class 'scoreparameters' 
data 
the data matrix with n columns (the number of variables) and a number of rows equal to the number of observations 
bgepar 
a list which contains parameters for BGe score:

bdepar 
a list which contains parameters for BDe score for binary data:

bdecatpar 
a list which contains parameters for BDe score for categorical data:

dbnpar 
which type of score to use for the slices

usrpar 
a list which contains parameters for the user defined score

mixedpar 
a list which contains parameters for the BGe and BDe score for mixed data

MDAG 
logical, when TRUE the score is initialized for a model with multiple sets of parameters but the same structure 
DBN 
logical, when TRUE the score is initialized for a dynamic Baysian network; FALSE by default 
weightvector 
(optional) a numerical vector of positive values representing the weight of each observation; should be NULL(default) for nonweighted data 
bgnodes 
(optional) a numerical vector which contains numbers of columns in the data defining background nodes, background nodes are nodes which have no parents but can be parents of other nodes in the network; in case of DBNs bgnodes represent static variables and defined via element b of the parameters dbnpar 
edgepmat 
(optional) a matrix of positive numerical values providing the per edge penalization factor to be added to the score, NULL by default 
nodeslabels 
(optional) a vector of characters which denote the names of nodes in the Bayesian network; by default column names of the data will be taken 
x 
object of class 'scoreparameters' 
... 
ignored 
object 
object of class 'scoreparameters' 
an object of class scoreparameters
, which includes all necessary information for calculating the BDe/BGe score
Polina Suter, Jack kuipers
Geiger D and Heckerman D (2002). Parameter priors for directed acyclic graphical models and the characterization of several probability distributions. The Annals of Statistics 30, 14121440.
Kuipers J, Moffa G and Heckerman D (2014). Addendum on the scoring of Gaussian acyclic graphical models. The Annals of Statistics 42, 16891691.
Heckerman D and Geiger D (1995). Learning Bayesian networks: A unification for discrete and Gaussian domains. In Eleventh Conference on Uncertainty in Artificial Intelligence, pages 274284.
Scutari M (2016). An EmpiricalBayes Score for Discrete Bayesian Networks. Journal of Machine Learning Research 52, 438448
myDAG<pcalg::randomDAG(20, prob=0.15, lB = 0.4, uB = 2) myData<pcalg::rmvDAG(200, myDAG) myScore<scoreparameters("bge", myData)