scoreContribution {abn} | R Documentation |
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 |
This function computes the score contribution of each observation
to the total network score.
This function is available only in the mle
settings.
To do so one uses the glm
and predict
functions.
This function is an attempt to perform diagnostic for an ABN analysis.
A named list that contains the scores contributions: maximum likelihood, aic, bic, mdl and diagonal values of the hat matrix.
## Not run:
## 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)
## End(Not run)