README.md

In coraallencoleman/ClusterRank: Complete Rankings with Mixture Model Clustering with Vizualizations

Clustered Rankings

An R package for creating complete rankings for a list of items with mixture model clustering & visualizations

Functions

ClusterRankBin Assigns ranks then clusters to each item in a list based on Binomial data. Calls npmleBin()

Args: y: number of binomial distributed events n: number of attempts k: number of starting clusters desired. Defaults to length(y) scale: scale on which to do ranking weighted: boolean indicating if inverse variance weighted is used n.iter: iterations used in EM algorithm n.samp: number of samples from posterior distribution row_names: optional row names argument

Returns: list including ranked_table, posterior, theta, pr_theta

ClusterRankPois Assigns ranks then clusters to each item in a list based on Poisson data. Calls npmlePois()

Args: y: number of poisson distributed events ti: time vector. Length of time. k: number of starting clusters desired. Defaults to length(y) scale: scale for ranking weighted: boolean indicating if inverse variance weighted is used n.iter: iterations used in EM algorithm n.samp: number of samples from posterior distribution row_names: optional row names argument

Returns: list including ranked_table, posterior, theta, pr_theta

ClusterRankNorm Assigns ranks then clusters to each item in a list based on Normal data. Calls npmleNorm()

Args: y: means for each item to be ranked se: standard errors for each mean y k: number of starting clusters desired. Defaults to length(y) scale: scale for ranking weighted: boolean indicating if inverse variance weighted is used n.iter: iterations used in EM algorithm n.samp: number of samples from posterior distribution row_names: optional row names argument

Returns: list including ranked_table, posterior, theta, pr_theta

PlotClusterRank(ClusterRank, xlab=NULL, maintitle=NULL) creates a visualization using the result of ClusterRankBin, ClusterRankPois or ClusterRankNorm. Shows ranks with clusters and confidence intervals of ranks.

Data Accepted

See data folder for examples.

Binomial Data requires: y count data, n trials optional: row names for items

Poisson Data requires: y counts, t time optional: row names for items

Normal Data requires: means, standard deviations optional: row names for items

Testing

To test using testthat package, open ClusterRank project and run: devtools::test()

coraallencoleman/ClusterRank documentation built on Oct. 13, 2019, 12:52 a.m.