groupbn: groupbn

In GroupBN: Inferring Group Bayesian Networks using Hierarchical Feature Clustering

Description

creates groupbn object (determines an initial clustering based on a hierarchy with target variable and 'separated' variables separated, learns a Bayesian network from grouped data and saves discretization and pca parameters)

Usage

groupbn(hierarchy, k, target, separate=NULL, separate.as.roots=FALSE,
X.quanti=NULL, X.quali=NULL, struct.alg="hc", boot=TRUE,
discretize=TRUE, arc.thresh=NULL,
debug=FALSE, R=100, seed=NULL)

Arguments

hierarchy	a cluster object from ClustOfVar.
k	a positive integer number, the number of initial clusters.
target	a string, the name of the target variable.
separate	a vector of strings, names of variables that should be separated from the groups, such as age, sex,...
separate.as.roots	a boolean; if TRUE separated variables are used as roots in the network. Can be ignored if separate is empty.
X.quanti	a numeric matrix of data, or an object that can be coerced to such a matrix (such as a numeric vector or a data frame with all numeric columns).
X.quali	a categorical matrix of data, or an object that can be coerced to such a matrix (such as a character vector, a factor or a data frame with all factor columns).
struct.alg	structure learning algorithm according to bnlearn
arc.thresh	threshold for bootstrap arcs
discretize	a boolean, if a network variables should be discretized before network learning
boot	boolean, if TRUE, a bootstrap based network averaging approach is used
debug	a boolean, if TRUE, debugging messages are printed
R	number of bootstrap replicates for model averaging, default is 100
seed	a random seed number

Value

an object of class groupbn

bn	a Bayesian Network structure of bn class from bnlearn.
fit	a Bayesian Network with fitted parameters of bn.fit class from bnlearn.
X.quanti	a data.frame containing only the quantitative variables.
X.quali	a data.frame containing only the qualitative variables.
grouping	a vector of positive integers, giving the cluster assignment.
k	the number of clusters.
group.data	a data.frame containing the cluster representants.
target	a string, the name of the target variable.
separate	a vector of strings, names of variables that should be separated from the groups.
pca.param	the PCAmix used to determine the cluster representants.
disc.param	the cutpoints used to discretize the cluster representants.
score	Different prediction scores for the target variable using the fitted network.

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_refinement

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)

#Plot network
plot(wine.groupbn)

GroupBN documentation built on March 7, 2021, 5:06 p.m.