Function reference • mclust

All functions

Baudry_etal_2010_JCGS_examples: Simulated Example Datasets From Baudry et al. (2010)

BrierScore(): Brier score to assess the accuracy of probabilistic predictions

EuroUnemployment: Unemployment data for European countries in 2014

GvHD: GvHD Dataset

Mclust(): Model-Based Clustering

MclustBootstrap(): Resampling-based Inference for Gaussian finite mixture models

MclustDA(): MclustDA discriminant analysis

MclustDR(): Dimension reduction for model-based clustering and classification

MclustDRsubsel(): Subset selection for GMMDR directions based on BIC

MclustSSC(): MclustSSC semi-supervised classification

acidity: Acidity data

adjustedRandIndex(): Adjusted Rand Index

banknote: Swiss banknotes data

bic(): BIC for Parameterized Gaussian Mixture Models

cdens(): Component Density for Parameterized MVN Mixture Models

cdensE() cdensV() cdensX() cdensEII() cdensVII() cdensEEI() cdensVEI() cdensEVI() cdensVVI() cdensEEE() cdensEEV() cdensVEV() cdensVVV() cdensEVE() cdensEVV() cdensVEE() cdensVVE() cdensXII() cdensXXI() cdensXXX(): Component Density for a Parameterized MVN Mixture Model

cdfMclust() quantileMclust(): Cumulative Distribution and Quantiles for a univariate Gaussian mixture distribution

chevron: Simulated minefield data

clPairs() clPairsLegend(): Pairwise Scatter Plots showing Classification

classError(): Classification error

classPriorProbs(): Estimation of class prior probabilities by EM algorithm

clustCombi(): Combining Gaussian Mixture Components for Clustering

clustCombiOptim(): Optimal number of clusters obtained by combining mixture components

combMat(): Combining Matrix

combiPlot(): Plot Classifications Corresponding to Successive Combined Solutions

combiTree(): Tree structure obtained from combining mixture components

coordProj(): Coordinate projections of multidimensional data modeled by an MVN mixture.

covw(): Weighted means, covariance and scattering matrices conditioning on a weighted matrix

crimcoords() summary(<crimcoords>) plot(<crimcoords>): Discriminant coordinates data projection

cross: Simulated Cross Data

cvMclustDA(): MclustDA cross-validation

decomp2sigma(): Convert mixture component covariances to matrix form

defaultPrior(): Default conjugate prior for Gaussian mixtures

dens(): Density for Parameterized MVN Mixtures

densityMclust(): Density Estimation via Model-Based Clustering

densityMclust.diagnostic(): Diagnostic plots for mclustDensity estimation

diabetes: Diabetes Data (flawed)

dmvnorm(): Density of multivariate Gaussian distribution

dupPartition(): Partition the data by grouping together duplicated data

em(): EM algorithm starting with E-step for parameterized Gaussian mixture models

emControl(): Set control values for use with the EM algorithm

emE() emV() emX() emEII() emVII() emEEI() emVEI() emEVI() emVVI() emEEE() emVEE() emEVE() emVVE() emEEV() emVEV() emEVV() emVVV() emXII() emXXI() emXXX(): EM algorithm starting with E-step for a parameterized Gaussian mixture model

entPlot(): Plot Entropy Plots

errorBars(): Draw error bars on a plot

estep(): E-step for parameterized Gaussian mixture models.

estepE() estepV() estepEII() estepVII() estepEEI() estepVEI() estepEVI() estepVVI() estepEEE() estepEEV() estepVEV() estepVVV() estepEVE() estepEVV() estepVEE() estepVVE(): E-step in the EM algorithm for a parameterized Gaussian mixture model.

gmmhd() plot(<gmmhd>): Identifying Connected Components in Gaussian Finite Mixture Models for Clustering

hc() as.hclust(<hc>): Model-based Agglomerative Hierarchical Clustering

hcE() hcV() hcEII() hcVII() hcEEE() hcVVV(): Model-based Hierarchical Clustering

hcRandomPairs(): Random hierarchical structure

hclass(): Classifications from Hierarchical Agglomeration

hdrlevels(): Highest Density Region (HDR) Levels

hypvol(): Aproximate Hypervolume for Multivariate Data

icl(): ICL for an estimated Gaussian Mixture Model

imputeData(): Missing data imputation via the mix package

imputePairs(): Pairwise Scatter Plots showing Missing Data Imputations

logLik(<Mclust>): Log-Likelihood of a Mclust object

logLik(<MclustDA>): Log-Likelihood of a MclustDA object

logsumexp(): Log sum of exponentials

majorityVote(): Majority vote

map(): Classification given Probabilities

mapClass(): Correspondence between classifications

cv.MclustDA() cv1EMtrain() bicEMtrain(): Deprecated Functions in mclust package

mclust-package mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

mclust.options(): Default values for use with MCLUST package

mclust1Dplot(): Plot one-dimensional data modeled by an MVN mixture.

mclust2Dplot(): Plot two-dimensional data modelled by an MVN mixture

mclustBIC(): BIC for Model-Based Clustering

mclustBICupdate(): Update BIC values for parameterized Gaussian mixture models

mclustBootstrapLRT() print(<mclustBootstrapLRT>) plot(<mclustBootstrapLRT>): Bootstrap Likelihood Ratio Test for the Number of Mixture Components

mclustICL() summary(<mclustICL>): ICL Criterion for Model-Based Clustering

mclustLoglik(): Log-likelihood from a table of BIC values for parameterized Gaussian mixture models

mclustModel(): Best model based on BIC

mclustModelNames(): MCLUST Model Names

mclustVariance(): Template for variance specification for parameterized Gaussian mixture models

me(): EM algorithm starting with M-step for parameterized MVN mixture models

me.weighted(): EM algorithm with weights starting with M-step for parameterized Gaussian mixture models

meE() meV() meX() meEII() meVII() meEEI() meVEI() meEVI() meVVI() meEEE() meVEE() meEVE() meVVE() meEEV() meVEV() meEVV() meVVV() meXII() meXXI() meXXX(): EM algorithm starting with M-step for a parameterized Gaussian mixture model

mstep(): M-step for parameterized Gaussian mixture models

mstepE() mstepV() mstepEII() mstepVII() mstepEEI() mstepVEI() mstepEVI() mstepVVI() mstepEEE() mstepEEV() mstepVEV() mstepVVV() mstepEVE() mstepEVV() mstepVEE() mstepVVE(): M-step for a parameterized Gaussian mixture model

mvn(): Univariate or Multivariate Normal Fit

mvnX() mvnXII() mvnXXI() mvnXXX(): Univariate or Multivariate Normal Fit

nMclustParams(): Number of Estimated Parameters in Gaussian Mixture Models

nVarParams(): Number of Variance Parameters in Gaussian Mixture Models

partconv(): Numeric Encoding of a Partitioning

partuniq(): Classifies Data According to Unique Observations

plot(<Mclust>): Plotting method for Mclust model-based clustering

plot(<MclustBootstrap>): Plot of bootstrap distributions for mixture model parameters

plot(<MclustDA>): Plotting method for MclustDA discriminant analysis

plot(<MclustDR>): Plotting method for dimension reduction for model-based clustering and classification

plot(<MclustSSC>): Plotting method for MclustSSC semi-supervised classification

plot(<clustCombi>): Plot Combined Clusterings Results

plot(<densityMclust>) plotDensityMclust1() plotDensityMclust2() plotDensityMclustd(): Plots for Mixture-Based Density Estimate

plot(<hc>): Dendrograms for Model-based Agglomerative Hierarchical Clustering

plot(<mclustBIC>): BIC Plot for Model-Based Clustering

plot(<mclustICL>): ICL Plot for Model-Based Clustering

predict(<Mclust>): Cluster multivariate observations by Gaussian finite mixture modeling

predict(<MclustDA>): Classify multivariate observations by Gaussian finite mixture modeling

predict(<MclustDR>): Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling

predict(<MclustSSC>): Classification of multivariate observations by semi-supervised Gaussian finite mixtures

predict(<densityMclust>): Density estimate of multivariate observations by Gaussian finite mixture modeling

priorControl(): Conjugate Prior for Gaussian Mixtures.

randProj(): Random projections of multidimensional data modeled by an MVN mixture

randomOrthogonalMatrix(): Random orthogonal matrix

sigma2decomp(): Convert mixture component covariances to decomposition form.

sim(): Simulate from Parameterized MVN Mixture Models

simE() simV() simEII() simVII() simEEI() simVEI() simEVI() simVVI() simEEE() simVEE() simEVE() simVVE() simEEV() simVEV() simEVV() simVVV(): Simulate from a Parameterized MVN Mixture Model

softmax(): Softmax function

summary(<Mclust>) print(<summary.Mclust>): Summarizing Gaussian Finite Mixture Model Fits

summary(<MclustBootstrap>): Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models

summary(<MclustDA>) print(<summary.MclustDA>): Summarizing discriminant analysis based on Gaussian finite mixture modeling

summary(<MclustDR>) print(<summary.MclustDR>): Summarizing dimension reduction method for model-based clustering and classification

summary(<MclustSSC>) print(<summary.MclustSSC>): Summarizing semi-supervised classification model based on Gaussian finite mixtures

summary(<mclustBIC>): Summary function for model-based clustering via BIC

surfacePlot(): Density or uncertainty surface for bivariate mixtures

thyroid: UCI Thyroid Gland Data

uncerPlot(): Uncertainty Plot for Model-Based Clustering

unmap(): Indicator Variables given Classification

wdbc: UCI Wisconsin Diagnostic Breast Cancer Data

wreath: Data Simulated from a 14-Component Mixture