Semi-Supervised Gaussian Mixture Model with a Missing-Data Mechanism

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Documentation for package ‘gmmsslm’ version 1.1.5

Help Pages

bayesclassifier Bayes' rule of allocation
bootstrap_gmmsslm Bootstrap Analysis for gmmsslm
cov2vec Transform a variance matrix into a vector
discriminant_beta Discriminant function
erate Error rate of the Bayes rule for a g-class Gaussian mixture model
errorrate Error rate of the Bayes rule for two-class Gaussian homoscedastic model
gastro_data Gastrointestinal dataset
get_clusterprobs Posterior probability
get_entropy Shannon entropy
gmmsslm Fitting Gaussian mixture model to a complete classified dataset or an incomplete classified dataset with/without the missing-data mechanism.
gmmsslmFit-class gmmsslmFit Class
initialvalue Initial values for ECM
list2par Transfer a list into a vector
loglk_full Full log-likelihood function
loglk_ig Log likelihood for partially classified data with ingoring the missing mechanism
loglk_miss Log likelihood function formed on the basis of the missing-label indicator
logsumexp log summation of exponential function
makelabelmatrix Label matrix
neg_objective_function Negative objective function for gmmssl
normalise_logprob Normalize log-probability
par2list Transfer a vector into a list
paraextract Extract parameter list from gmmsslmFit objects
paraextract-method Extract parameter list from gmmsslmFit objects
plot_missingness Plot Missingness Mechanism and Boxplot
predict Predict unclassified label
predict-method Predict unclassified label
pro2vec Transfer a probability vector into a vector
rlabel Generation of a missing-data indicator
rmix Normal mixture model generator.
summary Summary method for gmmsslmFit objects
summary-method Summary method for gmmsslmFit objects
vec2cov Transform a vector into a matrix
vec2pro Transfer an informative vector to a probability vector