Clusters features under the assumption that each cluster has a random effect and there is an outcome variable that is related to the random effects by a linear regression. In this way the cluster analysis is ``supervised'' by the outcome variable. An alternate specification is that features in each cluster have the same compound symmetric normal distribution, and the conditional distribution of the outcome given the features has the same coefficient for each feature in a cluster.
|Author||David A. Schoenfeld, Jesse Hsu|
|Date of publication||2015-05-18 22:45:09|
|Maintainer||David A. Schoenfeld <firstname.lastname@example.org>|
beta.by.gene: Utility to Associate the Value of beta with the Feature it is...
compare.chains: Compare Chains to Test Algorithm Coverage
concordmap: Calculate the Frequency with which each Pair of Features are...
gene_names: Trauma Data for Supervised Clustering
generate.cluster.data: Function to Generate Data According to the Supcluster Model
supcluster: Clustering of Features Supervised by an Outcome
supcluster-package: Supervised Cluster Anaysis
tab1: Simulates Supcluster Function
trauma_data: Trauma Data for Supervised Clustering