Model-Based Clustering / Normal Mixture Modeling

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Documentation for package `mclust' version 3.1-1

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A B C D E H M N P R S U W

.Mclust List of values controlling defaults for some MCLUST functions.

-- A --

adjustedRandIndex Adjusted Rand Index

-- B --

bic BIC for Parameterized Gaussian Mixture Models
bicEMtrain Select models in discriminant analysis using BIC

-- C --

cdens Component Density for Parameterized MVN Mixture Models
cdensE Component Density for a Parameterized MVN Mixture Model
cdensEEE Component Density for a Parameterized MVN Mixture Model
cdensEEI Component Density for a Parameterized MVN Mixture Model
cdensEEV Component Density for a Parameterized MVN Mixture Model
cdensEII Component Density for a Parameterized MVN Mixture Model
cdensEVI Component Density for a Parameterized MVN Mixture Model
cdensV Component Density for a Parameterized MVN Mixture Model
cdensVEI Component Density for a Parameterized MVN Mixture Model
cdensVEV Component Density for a Parameterized MVN Mixture Model
cdensVII Component Density for a Parameterized MVN Mixture Model
cdensVVI Component Density for a Parameterized MVN Mixture Model
cdensVVV Component Density for a Parameterized MVN Mixture Model
chevron Simulated minefield data
classError Classification error.
clPairs Pairwise Scatter Plots showing Classification
coordProj Coordinate projections of multidimensional data modeled by an MVN mixture.
cross Simulated Cross Data
cv1EMtrain Select discriminant models using cross validation

-- D --

decomp2sigma Convert mixture component covariances to matrix form.
defaultPrior Default conjugate prior for Gaussian mixtures.
Defaults.Mclust List of values controlling defaults for some MCLUST functions.
dens Density for Parameterized MVN Mixtures
diabetes Diabetes data

-- E --

em EM algorithm starting with E-step for parameterized Gaussian mixture models.
EMclust BIC for Model-Based Clustering
emControl Set control values for use with the EM algorithm.
emE EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emEEE EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emEEI EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emEEV EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emEII EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emEVI EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emV EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emVEI EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emVEV EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emVII EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emVVI EM algorithm starting with E-step for a parameterized Gaussian mixture model.
emVVV EM algorithm starting with E-step for a parameterized Gaussian mixture model.
estep E-step for parameterized Gaussian mixture models.
estepE E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepEEE E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepEEI E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepEEV E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepEII E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepEVI E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepV E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepVEI E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepVEV E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepVII E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepVVI E-step in the EM algorithm for a parameterized Gaussian mixture model.
estepVVV E-step in the EM algorithm for a parameterized Gaussian mixture model.

-- H --

hc Model-based Hierarchical Clustering
hcE Model-based Hierarchical Clustering
hcEEE Model-based Hierarchical Clustering
hcEII Model-based Hierarchical Clustering
hclass Classifications from Hierarchical Agglomeration
hcV Model-based Hierarchical Clustering
hcVII Model-based Hierarchical Clustering
hcVVV Model-based Hierarchical Clustering
hypvol Aproximate Hypervolume for Multivariate Data

-- M --

map Classification given Probabilities
mapClass Correspondence between classifications.
Mclust Model-Based Clustering
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
mclustDA MclustDA discriminant analysis.
mclustDAtest MclustDA Testing
mclustDAtrain MclustDA Training
mclustModel Best model based on BIC.
mclustModelNames MCLUST Model Names
mclustOptions Set default values for use with MCLUST.
mclustVariance Template for variance specification for parameterized Gaussian mixture models.
me EM algorithm starting with M-step for parameterized MVN mixture models.
meE EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meEEE EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meEEI EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meEEV EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meEII EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meEVI EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meV EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meVEI EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meVEV EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meVII EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meVVI EM algorithm starting with M-step for a parameterized Gaussian mixture model.
meVVV EM algorithm starting with M-step for a parameterized Gaussian mixture model.
mstep M-step for parameterized Gaussian mixture models.
mstepE M-step for a parameterized Gaussian mixture model.
mstepEEE M-step for a parameterized Gaussian mixture model.
mstepEEI M-step for a parameterized Gaussian mixture model.
mstepEEV M-step for a parameterized Gaussian mixture model.
mstepEII M-step for a parameterized Gaussian mixture model.
mstepEVI M-step for a parameterized Gaussian mixture model.
mstepV M-step for a parameterized Gaussian mixture model.
mstepVEI M-step for a parameterized Gaussian mixture model.
mstepVEV M-step for a parameterized Gaussian mixture model.
mstepVII M-step for a parameterized Gaussian mixture model.
mstepVVI M-step for a parameterized Gaussian mixture model.
mstepVVV M-step for a parameterized Gaussian mixture model.
mvn Univariate or Multivariate Normal Fit
mvnX Univariate or Multivariate Normal Fit
mvnXII Univariate or Multivariate Normal Fit
mvnXXI Univariate or Multivariate Normal Fit
mvnXXX Univariate or Multivariate Normal Fit

-- N --

nVarParams Number of Variance Parameters in Gaussian Mixture Models

-- P --

partconv Numeric Encoding of a Partitioning
partuniq Classifies Data According to Unique Observations
plot.Mclust Plot Model-Based Clustering Results
plot.mclustBIC BIC Plot
plot.mclustDA Plotting method for MclustDA discriminant analysis.
plot.mclustDAtrain Plot mclustDA training models.
print.Mclust Model-Based Clustering
print.mclustBIC BIC for Model-Based Clustering
print.mclustDA MclustDA discriminant analysis.
print.mclustDAtrain MclustDA Training
print.summary.mclustBIC Summary Function for model-based clustering.
printSummaryMclustBIC Summary Function for model-based clustering.
printSummaryMclustBICn Summary Function for model-based clustering.
priorControl Conjugate Prior for Gaussian Mixtures.

-- R --

randProj Random projections of multidimensional data modeled by an MVN mixture.

-- S --

sigma2decomp Convert mixture component covariances to decomposition form.
sim Simulate from Parameterized MVN Mixture Models
simE Simulate from a Parameterized MVN Mixture Model
simEEE Simulate from a Parameterized MVN Mixture Model
simEEI Simulate from a Parameterized MVN Mixture Model
simEEV Simulate from a Parameterized MVN Mixture Model
simEII Simulate from a Parameterized MVN Mixture Model
simEVI Simulate from a Parameterized MVN Mixture Model
simV Simulate from a Parameterized MVN Mixture Model
simVEI Simulate from a Parameterized MVN Mixture Model
simVEV Simulate from a Parameterized MVN Mixture Model
simVII Simulate from a Parameterized MVN Mixture Model
simVVI Simulate from a Parameterized MVN Mixture Model
simVVV Simulate from a Parameterized MVN Mixture Model
summary.mclustBIC Summary Function for model-based clustering.
summary.mclustDAtest Classification and posterior probability from mclustDAtest.
summary.mclustDAtrain Models and classifications from mclustDAtrain
summary.mclustModel Summary Function for MCLUST Models
summaryMclustBIC Summary Function for model-based clustering.
summaryMclustBICn Summary Function for model-based clustering.
surfacePlot Density or uncertainty surface for two dimensional mixtures.

-- U --

uncerPlot Uncertainty Plot for Model-Based Clustering
unmap Indicator Variables given Classification

-- W --

wreath Data Simulated from a 14-Component Mixture