groupbn_refinement {GroupBN}

groupbn_refinement

Description

Adaptive Refinement of a group Bayesian Network using hierarchical Clustering

Usage

groupbn_refinement(res, hierarchy, refinement.part="mb", restart=0, perturb=1,
max.step=10, max.min=Inf, R=100,
return.all=FALSE, arc.thresh=NULL, debug=FALSE, seed=NULL)

Arguments

res

an object of class groupbn

hierarchy

a cluster object from ClustOfVar

refinement.part

"mb", "mb2", "arc.confid" or "all", selects if the refinement steps should be done only within the markov blanket of the target variable (mb), within the second-order markov blanket (mb2), in all clusters with an arcconfidence to target >0 (arc.confid) or within all clusters (all). Default: "mb"

restart

a positive integer number, the number of restarts

perturb

a positive integer number, the number of perturbations (splits) in each restart

max.step

a positive integer number, the maximal number of refinement steps, default is 10

max.min

a positive integer number, the maximal run time in minutes, default is unlimited

R

number of bootstrap replicates for model averaging, default is 100

return.all

a boolean, if TRUE, the output is a whole list of group models, if FALSE, the output is only the best-scoring model.

arc.thresh

threshold for bootstrap arcs

debug

a boolean, if TRUE, debugging messages are printed

seed

a random seed number

Details

Based on a variable grouping, data are aggregated and a Bayesian network is learned. The target variable is kept separated during this procedure, so that the resulting network model can be used for risk prediction and classification. Starting from a coarse group network, groups are iteratively refined to smaller groups. The heuristic refinement happens downwards along the dendrogram, and stops, if it no longer improves the predictive performance of the model. The refinement part is implemented using a hill-climbing procedure.

Value

returns an object of class groupbn

Author(s)

Ann-Kristin Becker

References

Becker A-K, Dörr M, Felix SB, Frost F, Grabe HJ, Lerch MM, et al. (2021) From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach. PLoS Comput Biol 17(2): e1008735. https://doi.org/10.1371/journal.pcbi.1008735

See Also

groupbn groupbn.output.table

Examples

#load example data
data(wine)
wine.test<-wine[wine$Soil%in%c("Reference", "Env1"),1:29]
wine.test$Soil<-factor(wine.test$Soil)
levels(wine.test$Soil)<-c("0", "1")

#cluster data
hierarchy<-hclustvar(X.quanti=wine.test[,3:29], X.quali=wine.test[,1:2])

#Learn group network among 5 clusters with "Soil" as target variable
wine.groupbn<-groupbn(hierarchy, k=5, target="Soil", separate=NULL,
X.quanti=wine.test[,3:29], X.quali=wine.test[,1:2], seed=321)

#Do one refinement step
#Set max.step higher to optimize completely
wine.groupbn.refined<-groupbn_refinement(wine.groupbn, hierarchy,
refinement.part="mb", max.step = 1, seed=321)

#Plot refined network
plot(wine.groupbn.refined)