Compute the score's contribution in a network of each observation.
This function computes the score's contribution of each observation to the total network score.
scoreContribution(object = NULL, dag = NULL, data.df = NULL, data.dists = NULL, verbose = FALSE)
object |
an object of class ' |
dag |
a matrix or a formula statement (see details) defining the network structure, a directed acyclic graph (DAG), see details for format. Note that colnames and rownames must be set. |
data.df |
a data frame containing the data used for learning the network, binary variables must be declared as factors and no missing values all allowed in any variable. |
data.dists |
a named list giving the distribution for each node in the network, see details. |
verbose |
if |
A named list that contains the scores contributions: maximum likelihood, aic, bic, mdl and diagonal values of the hat matrix.
Gilles Kratzer
## Use a subset of a built-in simulated data set mydat <- ex1.dag.data[,c("b1","g1","p1")] ## setup distribution list for each node mydists <- list(b1="binomial", g1="gaussian", p1="poisson") ## now build cache mycache <- buildScoreCache(data.df = mydat, data.dists = mydists, max.parents = 2, method = "mle") ## Find the globally best DAG mp.dag <- mostProbable(score.cache=mycache, score="bic", verbose = FALSE) out <- scoreContribution(object = mp.dag) ## Observations contribution per network node boxplot(out$bic)
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