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RPMM (version 1.20)

Recursively Partitioned Mixture Model

Description

Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures. This is a model-based clustering algorithm that returns a hierarchy of classes, similar to hierarchical clustering, but also similar to finite mixture models.

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Version

Install

install.packages('RPMM')

Monthly Downloads

1,822

Version

1.20

License

GPL (>= 2)

Maintainer

E Andres Houseman

Last Published

September 14th, 2014

Functions in RPMM (1.20)

Beta Distribution Maximum Likelihood Estimator

IlluminaMethylation

DNA Methylation Data for Normal Tissue Types

blcTreeLeafClasses

Posterior Class Assignments for Beta RPMM

blcTreeLeafMatrix

Posterior Weight Matrix for Beta RPMM

blcSplitCriterionBICICL

Beta RPMM Split Criterion: Use ICL-BIC

blcInitializeSplitHClust

Initialize Beta Latent Class via Hierarchical Clustering

Recursive Apply Function for Beta RPMM Objects

glcSplitCriterionBICICL

Gaussian RPMM Split Criterion: Use ICL-BIC

glcInitializeSplitEigen

Initialize Gaussian Latent Class via Eigendecomposition

Plot a Beta RPMM Tree Profile

Weighted GLM for latent class covariates

blcTreeOverallBIC

Overall BIC for Entire RPMM Tree (Beta version)

glcInitializeSplitHClust

Initialize Gaussian Latent Class via Hierarchical Clustering

llikeRPMMObject

Data log-likelihood implied by a specific RPMM model

Gaussian Subtree

Beta Latent Class Splitter

blcSplitCriterionJustRecordEverything

Beta RPMM Split Criterion: Always Split and Record Everything

Print a Beta RPMM object

Empirical Bayes predictions for a specific RPMM model

blcSplitCriterionLevelWtdBIC

Beta RPMM Split Criterion: Level-Weighted BIC

plotTree.glcTree

Plot a Gaussian RPMM Tree Dendrogram

glcTreeOverallBIC

Overall BIC for Entire RPMM Tree (Gaussian version)

glcSplitCriterionJustRecordEverything

Gaussian RPMM Split Criterion: Always Split and Record Everything

gaussEstMultiple

Gaussian Maximum Likelihood on a Matrix

betaEstMultiple

Beta Maximum Likelihood on a Matrix

blcInitializeSplitDichotomizeUsingMean

Initialize Gaussian Latent Class via Mean Dichotomization

glcTreeLeafMatrix

Posterior Weight Matrix for Gaussian RPMM

predict.glcTree

Predict using a Gaussian RPMM object

glcSplitCriterionLevelWtdBIC

Gaussian RPMM Split Criterion: Level-Weighted BIC

Beta Maximum Likelihood Objective Function

Beta Latent Class Model

plotImage.glcTree

Plot a Gaussian RPMM Tree Profile

glcSplitCriterionBIC

Gaussian RPMM Split Criterion: Use BIC

glcSplitCriterionLRT

Gaussian RPMM Split Criterion: Use likelihood ratio test p value

blcSplitCriterionLRT

Beta RPMM Split Criterion: use likelihood ratio test p value

blcInitializeSplitFanny

Initialize Beta Latent Class via Fanny

Gaussian Finite Mixture Model

plotImage.blcTree

Plot a Beta RPMM Tree Profile

plotTree.blcTree

Plot a Beta RPMM Tree Dendrogram

Gaussian Latent Class Splitter

blcInitializeSplitEigen

Initialize Gaussian Latent Class via Eigendecomposition

Print a Gaussian RPMM object

glcInitializeSplitFanny

Initialize Gaussian Latent Class via Fanny

blcSplitCriterionBIC

Beta RPMM Split Criterion: Use BIC

Recursive Apply Function for Gaussian RPMM Objects

glcTreeLeafClasses

Posterior Class Assignments for Gaussian RPMM

Plot a Gaussian RPMM Tree Profile

predict.blcTree

Predict using a Beta RPMM object

Gaussian RPMM Tree