Package 'MGMM' reference manual

Title:	Missingness Aware Gaussian Mixture Models
Description:	Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "Fitting Gaussian mixture models on incomplete data." <doi:10.1186/s12859-022-04740-9>.
Authors:	Zachary McCaw [aut, cre]
Maintainer:	Zachary McCaw <[email protected]>
License:	GPL-3
Version:	1.0.1.1
Built:	2024-11-23 06:39:39 UTC
Source:	CRAN

Function to choose the number of clusters k. Examines cluster numbers between k0 and k1. For each cluster number, generates boot bootstrap data sets, fits the Gaussian Mixture Model (FitGMM), and calculates quality metrics (ClustQual). For each metric, determines the optimal cluster number k_opt, and the k_1SE, the smallest cluster number whose quality is within 1 SE of the optimum.

Usage

ChooseK(
  data,
  k0 = 2,
  k1 = NULL,
  boot = 100,
  init_means = NULL,
  fix_means = FALSE,
  init_covs = NULL,
  init_props = NULL,
  maxit = 10,
  eps = 1e-04,
  report = TRUE
)
ChooseK(
  data,
  k0 = 2,
  k1 = NULL,
  boot = 100,
  init_means = NULL,
  fix_means = FALSE,
  init_covs = NULL,
  init_props = NULL,
  maxit = 10,
  eps = 1e-04,
  report = TRUE
)

Arguments

`data`	Numeric data matrix.
`k0`	Minimum number of clusters.
`k1`	Maximum number of clusters.
`boot`	Bootstrap replicates.
`init_means`	Optional list of initial mean vectors.
`fix_means`	Fix the means to their starting value? Must provide initial values.
`init_covs`	Optional list of initial covariance matrices.
`init_props`	Optional vector of initial cluster proportions.
`maxit`	Maximum number of EM iterations.
`eps`	Minimum acceptable increment in the EM objective.
`report`	Report bootstrap progress?

Value

List containing Choices, the recommended number of clusters according to each quality metric, and Results, the mean and standard error of the quality metrics at each cluster number evaluated.

Examples


set.seed(100)
mean_list <- list(c(2, 2), c(2, -2), c(-2, 2), c(-2, -2))
data <- rGMM(n = 500, d = 2, k = 4, means = mean_list)
choose_k <- ChooseK(data, k0 = 2, k1 = 6, boot = 10)
choose_k$Choices

set.seed(100)
mean_list <- list(c(2, 2), c(2, -2), c(-2, 2), c(-2, -2))
data <- rGMM(n = 500, d = 2, k = 4, means = mean_list)
choose_k <- ChooseK(data, k0 = 2, k1 = 6, boot = 10)
choose_k$Choices

Cluster Quality

Description

Evaluates cluster quality. Returns the following metrics:

BIC: Bayesian Information Criterion, lower value indicates better clustering quality.
CHI: Calinski-Harabaz Index, higher value indicates better clustering quality.
DBI: Davies-Bouldin, lower value indicates better clustering quality.
SIL: Silhouette Width, higher value indicates better clustering quality.

Usage

ClustQual(fit)
ClustQual(fit)

Arguments

fit

Object of class mix.

Value

List containing the cluster quality metrics.

Examples

set.seed(100)

# Data generation
mean_list = list(
c(2, 2, 2),
c(-2, 2, 2),
c(2, -2, 2),
c(2, 2, -2)
)

data <- rGMM(n = 500, d = 3, k = 4, means = mean_list)
fit <- FitGMM(data, k = 4)

# Clustering quality
cluster_qual <- ClustQual(fit)
set.seed(100)

# Data generation
mean_list = list(
c(2, 2, 2),
c(-2, 2, 2),
c(2, -2, 2),
c(2, 2, -2)
)

data <- rGMM(n = 500, d = 3, k = 4, means = mean_list)
fit <- FitGMM(data, k = 4)

# Clustering quality
cluster_qual <- ClustQual(fit)

Combine Multiple Imputations

Description

Combines point estimates and standard errors across multiple imputations.

Usage

CombineMIs(points, covs)
CombineMIs(points, covs)

Arguments

`points`	List of point estimates, potentially vector valued.
`covs`	List of sampling covariances, potentially matrix valued.

Value

List containing the final point estimate ('point') and sampling covariance ('cov').

Examples

set.seed(100)

# Generate data and introduce missingness.
data <- rGMM(n = 25, d = 2, k = 1)
data[1, 1] <- NA
data[2, 2] <- NA
data[3, ] <- NA 

# Fit GMM.
fit <- FitGMM(data)

# Lists to store summary statistics.
points <- list()
covs <- list()

# Perform 50 multiple imputations.
# For each, calculate the marginal mean and its sampling variance.
for (i in seq_len(50)) {
  imputed <- GenImputation(fit)
  points[[i]] <- apply(imputed, 2, mean)
  covs[[i]] <- cov(imputed) / nrow(imputed)
}

# Combine summary statistics across imputations.
results <- CombineMIs(points, covs)
set.seed(100)

# Generate data and introduce missingness.
data <- rGMM(n = 25, d = 2, k = 1)
data[1, 1] <- NA
data[2, 2] <- NA
data[3, ] <- NA 

# Fit GMM.
fit <- FitGMM(data)

# Lists to store summary statistics.
points <- list()
covs <- list()

# Perform 50 multiple imputations.
# For each, calculate the marginal mean and its sampling variance.
for (i in seq_len(50)) {
  imputed <- GenImputation(fit)
  points[[i]] <- apply(imputed, 2, mean)
  covs[[i]] <- cov(imputed) / nrow(imputed)
}

# Combine summary statistics across imputations.
results <- CombineMIs(points, covs)

Davies-Bouldin Index

Description

Calculates the Davies-Bouldin index.

Usage

DavBou(data, assign, means)
DavBou(data, assign, means)

Arguments

`data`	Observations
`assign`	Assignments
`means`	List of cluster means

Value

Scalar index.

Estimate Multivariate Normal Mixture

Description

Given an $n \times d$ matrix of random vectors, estimates the parameters of a Gaussian Mixture Model (GMM). Accommodates arbitrary patterns of missingness at random (MAR) in the input vectors.

Usage

FitGMM(
  data,
  k = 1,
  init_means = NULL,
  fix_means = FALSE,
  init_covs = NULL,
  init_props = NULL,
  maxit = 100,
  eps = 1e-06,
  report = TRUE
)
FitGMM(
  data,
  k = 1,
  init_means = NULL,
  fix_means = FALSE,
  init_covs = NULL,
  init_props = NULL,
  maxit = 100,
  eps = 1e-06,
  report = TRUE
)

Arguments

`data`	Numeric data matrix.
`k`	Number of mixture components. Defaults to 1.
`init_means`	Optional list of initial mean vectors.
`fix_means`	Fix the means to their starting value? Must provide initial values.
`init_covs`	Optional list of initial covariance matrices.
`init_props`	Optional vector of initial cluster proportions.
`maxit`	Maximum number of EM iterations.
`eps`	Minimum acceptable increment in the EM objective.
`report`	Report fitting progress?

Details

Initial values for the cluster means, covariances, and proportions are specified using M0, S0, and pi0, respectively. If the data contains complete observations, i.e. observations with no missing elements, then fit.GMM will attempt to initialize these parameters internally using K-means. If the data contains no complete observations, then initial values are required for M0, S0, and pi0.

Value

For a single component, an object of class mvn, containing the estimated mean and covariance, the final objective function, and the imputed data.
For a multicomponent model $k>1$ , an object of class mix, containing the estimated means, covariances, cluster proportions, cluster responsibilities, and observation assignments.

Examples


# Single component without missingness
# Bivariate normal observations
sigma <- matrix(c(1, 0.5, 0.5, 1), nrow = 2)
data <- rGMM(n = 1e3, d = 2, k = 1, means = c(2, 2), covs = sigma)
fit <- FitGMM(data, k = 1)

# Single component with missingness
# Trivariate normal observations
mean_list <- list(c(-2, -2, -2), c(2, 2, 2))
sigma <- matrix(c(1, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 1), nrow = 3)
data <- rGMM(n = 1e3, d = 3, k = 2, means = mean_list, covs = sigma)
fit <- FitGMM(data, k = 2)

# Two components without missingness
# Trivariate normal observations
mean_list <- list(c(-2, -2, -2), c(2, 2, 2))
sigma <- matrix(c(1, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 1), nrow = 3)
data <- rGMM(n = 1e3, d = 3, k = 2, means = mean_list, covs = sigma)
fit <- FitGMM(data, k = 2)

# Four components with missingness
# Bivariate normal observations
# Note: Fitting is slow.
mean_list <- list(c(2, 2), c(2, -2), c(-2, 2), c(-2, -2))
sigma <- 0.5 * diag(2)
data <- rGMM(
n = 1000, 
d = 2, 
k = 4, 
pi = c(0.35, 0.15, 0.15, 0.35), 
m = 0.1, 
means = mean_list, 
covs = sigma)
fit <- FitGMM(data, k = 4)

# Single component without missingness
# Bivariate normal observations
sigma <- matrix(c(1, 0.5, 0.5, 1), nrow = 2)
data <- rGMM(n = 1e3, d = 2, k = 1, means = c(2, 2), covs = sigma)
fit <- FitGMM(data, k = 1)

# Single component with missingness
# Trivariate normal observations
mean_list <- list(c(-2, -2, -2), c(2, 2, 2))
sigma <- matrix(c(1, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 1), nrow = 3)
data <- rGMM(n = 1e3, d = 3, k = 2, means = mean_list, covs = sigma)
fit <- FitGMM(data, k = 2)

# Two components without missingness
# Trivariate normal observations
mean_list <- list(c(-2, -2, -2), c(2, 2, 2))
sigma <- matrix(c(1, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 1), nrow = 3)
data <- rGMM(n = 1e3, d = 3, k = 2, means = mean_list, covs = sigma)
fit <- FitGMM(data, k = 2)

# Four components with missingness
# Bivariate normal observations
# Note: Fitting is slow.
mean_list <- list(c(2, 2), c(2, -2), c(-2, 2), c(-2, -2))
sigma <- 0.5 * diag(2)
data <- rGMM(
n = 1000, 
d = 2, 
k = 4, 
pi = c(0.35, 0.15, 0.15, 0.35), 
m = 0.1, 
means = mean_list, 
covs = sigma)
fit <- FitGMM(data, k = 4)

Fit Multivariate Mixture Distribution

Description

Given a matrix of random vectors, estimates the parameters for a mixture of multivariate normal distributions. Accommodates arbitrary patterns of missingness, provided the elements are missing at random (MAR).

Usage

FitMix(
  data,
  k = 2,
  init_means = NULL,
  fix_means = FALSE,
  init_covs = NULL,
  init_props = NULL,
  maxit = 100,
  eps = 1e-06,
  report = FALSE
)
FitMix(
  data,
  k = 2,
  init_means = NULL,
  fix_means = FALSE,
  init_covs = NULL,
  init_props = NULL,
  maxit = 100,
  eps = 1e-06,
  report = FALSE
)

Arguments

`data`	Numeric data matrix.
`k`	Number of mixture components. Defaults to 2.
`init_means`	Optional list of initial mean vectors.
`fix_means`	Fix means to their starting values? Must initialize.
`init_covs`	Optional list of initial covariance matrices.
`init_props`	Optional vector of initial cluster proportions.
`maxit`	Maximum number of EM iterations.
`eps`	Minimum acceptable increment in the EM objective.
`report`	Report fitting progress?

Value

Object of class mix.

Fit Multivariate Normal Distribution

Description

Given a matrix of n x d-dimensional random vectors, possibly containing missing elements, estimates the mean and covariance of the best fitting multivariate normal distribution.

Usage

FitMVN(
  data,
  init_mean = NULL,
  fix_mean = FALSE,
  init_cov = NULL,
  maxit = 100,
  eps = 1e-06,
  report = TRUE
)
FitMVN(
  data,
  init_mean = NULL,
  fix_mean = FALSE,
  init_cov = NULL,
  maxit = 100,
  eps = 1e-06,
  report = TRUE
)

Arguments

`data`	Numeric data matrix.
`init_mean`	Optional initial mean vector.
`fix_mean`	Fix the mean to its starting value? Must initialize.
`init_cov`	Optional initial covariance matrix.
`maxit`	Maximum number of EM iterations.
`eps`	Minimum acceptable increment in the EM objective.
`report`	Report fitting progress?

Value

An object of class mvn.

Generate Imputation

Description

Generates a stochastic imputation of a data set from a fitted data set.

Usage

GenImputation(fit)
GenImputation(fit)

Arguments

fit

Fitted model.

Value

Numeric matrix with missing values imputed.

Examples

set.seed(100)

# Generate data and introduce missingness.
data <- rGMM(n = 25, d = 2, k = 1)
data[1, 1] <- NA
data[2, 2] <- NA
data[3, ] <- NA 

# Fit GMM.
fit <- FitGMM(data)

# Generate imputation.
imputed <- GenImputation(fit)
set.seed(100)

# Generate data and introduce missingness.
data <- rGMM(n = 25, d = 2, k = 1)
data[1, 1] <- NA
data[2, 2] <- NA
data[3, ] <- NA 

# Fit GMM.
fit <- FitGMM(data)

# Generate imputation.
imputed <- GenImputation(fit)

Log likelihood for Fitted GMM

Description

Log likelihood for Fitted GMM

Usage

## S3 method for class 'mix'
logLik(object, ...)
## S3 method for class 'mix'
logLik(object, ...)

Arguments

`object`	A `mix` object.
`...`	Unused.

Log likelihood for Fitted MVN Model

Description

Log likelihood for Fitted MVN Model

Usage

## S3 method for class 'mvn'
logLik(object, ...)
## S3 method for class 'mvn'
logLik(object, ...)

Arguments

`object`	A `mvn` object.
`...`	Unused.

Mean for Fitted GMM

Description

Mean for Fitted GMM

Usage

## S3 method for class 'mix'
mean(x, ...)
## S3 method for class 'mix'
mean(x, ...)

Arguments

`x`	A `mix` object.
`...`	Unused.

Mean for Fitted MVN Model

Description

Mean for Fitted MVN Model

Usage

## S3 method for class 'mvn'
mean(x, ...)
## S3 method for class 'mvn'
mean(x, ...)

Arguments

`x`	A `mvn` object.
`...`	Unused.

Mixture Model Class

Description

Defines a class to hold Gaussian Mixture Models.

Slots

Assignments: Maximum a posteriori assignments.
Completed: Completed data, with missing values imputed to their posterior expectations.
Components: Number of components.
Covariances: List of fitted cluster covariance matrices.
Data: Original data, with missing values present.
Density: Density of each component at each example.
Means: List of fitted cluster means.
Objective: Final value of the EM objective.
Proportions: Fitted cluster proportions.
Responsibilities: Posterior membership probabilities for each example.

Mean Update for Mixture of MVNs with Missingness.

Description

Mean Update for Mixture of MVNs with Missingness.

Usage

MixUpdateMeans(split_data, means, covs, gamma)
MixUpdateMeans(split_data, means, covs, gamma)

Arguments

`split_data`	Data partitioned by missingness.
`means`	List of component means.
`covs`	List of component covariances.
`gamma`	List of component responsibilities.

Value

List containing the updated component means.

Multivariate Normal Model Class

Description

Defines a class to hold multivariate normal models.

Slots

Completed: Completed data, with missing values imputed to their posterior expectations.
Covariance: Fitted covariance matrix.
Data: Original data, with missing values present.
Mean: Fitted mean vector.
Objective: Final value of the EM objective.

Partition Data by Missingness Pattern

Description

Returns a list with the input data split in separate matrices for complete cases, incomplete cases, and empty cases.

Usage

PartitionData(data)
PartitionData(data)

Arguments

data

Data.frame.

Value

List containing:

The original row and column names: 'orig_row_names', 'orig_col_names'.
The original row and column numbers: 'n_row' and 'n_col'.
The complete cases 'data_comp'.
The incomplete cases 'data_incomp'.
The empty cases 'data_empty'.
Counts of complete 'n0', incomplete 'n1', and empty 'n2' cases.
Initial order of the observations 'init_order'.

Print for Fitted GMM

Description

Print method for objects of class mix.

Usage

## S3 method for class 'mix'
print(x, ...)
## S3 method for class 'mix'
print(x, ...)

Arguments

`x`	A `mix` object.
`...`	Unused.

Print for Fitted MVN Model

Description

Print for Fitted MVN Model

Usage

## S3 method for class 'mvn'
print(x, ...)
## S3 method for class 'mvn'
print(x, ...)

Arguments

`x`	A `mvn` object.
`...`	Unused.

Reconstitute Data

Description

Reassembles a data matrix split by missingness pattern.

Usage

ReconstituteData(split_data)
ReconstituteData(split_data)

Arguments

split_data

Split data are returned by PartitionData.

Value

Numeric matrix.

Generate Data from Gaussian Mixture Models

Description

Generates an $n\times d$ matrix of multivariate normal random vectors with observations (examples) as rows. If $k=1$ , all observations belong to the same cluster. If $k>1$ the observations are generated via two-step procedure. First, the cluster membership is drawn from a multinomial distribution, with mixture proportions specified by pi. Conditional on cluster membership, the observation is drawn from a multivariate normal distribution, with cluster-specific mean and covariance. The cluster means are provided using means, and the cluster covariance matrices are provided using covs. If $miss>0$ , missingness is introduced, completely at random, by setting that proportion of elements in the data matrix to NA.

Usage

rGMM(n, d = 2, k = 1, pi = NULL, miss = 0, means = NULL, covs = NULL)
rGMM(n, d = 2, k = 1, pi = NULL, miss = 0, means = NULL, covs = NULL)

Arguments

`n`	Observations (rows).
`d`	Observation dimension (columns).
`k`	Number of mixture components. Defaults to 1.
`pi`	Mixture proportions. If omitted, components are assumed equiprobable.
`miss`	Proportion of elements missing, $miss\in[0,1)$ .
`means`	Either a prototype mean vector, or a list of mean vectors. Defaults to the zero vector.
`covs`	Either a prototype covariance matrix, or a list of covariance matrices. Defaults to the identity matrix.

Value

Numeric matrix with observations as rows. Row numbers specify the true cluster assignments.

Examples

set.seed(100)
# Single component without missingness.
# Bivariate normal observations.
cov <- matrix(c(1, 0.5, 0.5, 1), nrow = 2)
data <- rGMM(n = 1e3, d = 2, k = 1, means = c(2, 2), covs = cov)

# Single component with missingness.
# Trivariate normal observations.
mean_list <- list(c(-2, -2, -2), c(2, 2, 2))
cov <- matrix(c(1, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 1), nrow = 3)
data <- rGMM(n = 1e3, d = 3, k = 2, means = mean_list, covs = cov)

# Two components without missingness.
# Trivariate normal observations.
mean_list <- list(c(-2, -2, -2), c(2, 2, 2))
cov <- matrix(c(1, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 1), nrow = 3)
data <- rGMM(n = 1e3, d = 3, k = 2, means = mean_list, covs = cov)

# Four components with missingness.
# Bivariate normal observations.
mean_list <- list(c(2, 2), c(2, -2), c(-2, 2), c(-2, -2))
cov <- 0.5 * diag(2)
data <- rGMM(
n = 1000, 
d = 2, 
k = 4, 
pi = c(0.35, 0.15, 0.15, 0.35), 
miss = 0.1, 
means = mean_list, 
covs = cov)
set.seed(100)
# Single component without missingness.
# Bivariate normal observations.
cov <- matrix(c(1, 0.5, 0.5, 1), nrow = 2)
data <- rGMM(n = 1e3, d = 2, k = 1, means = c(2, 2), covs = cov)

# Single component with missingness.
# Trivariate normal observations.
mean_list <- list(c(-2, -2, -2), c(2, 2, 2))
cov <- matrix(c(1, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 1), nrow = 3)
data <- rGMM(n = 1e3, d = 3, k = 2, means = mean_list, covs = cov)

# Two components without missingness.
# Trivariate normal observations.
mean_list <- list(c(-2, -2, -2), c(2, 2, 2))
cov <- matrix(c(1, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 1), nrow = 3)
data <- rGMM(n = 1e3, d = 3, k = 2, means = mean_list, covs = cov)

# Four components with missingness.
# Bivariate normal observations.
mean_list <- list(c(2, 2), c(2, -2), c(-2, 2), c(-2, -2))
cov <- 0.5 * diag(2)
data <- rGMM(
n = 1000, 
d = 2, 
k = 4, 
pi = c(0.35, 0.15, 0.15, 0.35), 
miss = 0.1, 
means = mean_list, 
covs = cov)

Show for Fitted Mixture Models

Description

Show for Fitted Mixture Models

Usage

## S4 method for signature 'mix'
show(object)
## S4 method for signature 'mix'
show(object)

Arguments

object

A mix object.

Show for Multivariate Normal Models

Description

Show for Multivariate Normal Models

Usage

## S4 method for signature 'mvn'
show(object)
## S4 method for signature 'mvn'
show(object)

Arguments

object

A mvn object.

Covariance for Fitted GMM

Description

Covariance for Fitted GMM

Usage

## S3 method for class 'mix'
vcov(object, ...)
## S3 method for class 'mix'
vcov(object, ...)

Arguments

`object`	A `mix` object.
`...`	Unused.

Covariance for Fitted MVN Model

Description

Covariance for Fitted MVN Model

Usage

## S3 method for class 'mvn'
vcov(object, ...)
## S3 method for class 'mvn'
vcov(object, ...)

Arguments

`object`	A `mvn` object.
`...`	Unused.

`data`	Observations.
`assign`	Assignments.
`means`	List of cluster means.

Package 'MGMM'

Help Index

Calinski-Harabaz Index

Description

Usage

Arguments

Value

Cluster Number Selection

Description

Usage

Arguments

Value

See Also

Examples

Cluster Quality

Description

Usage

Arguments

Value

See Also

Examples

Combine Multiple Imputations

Description

Usage

Arguments

Value

Examples

Davies-Bouldin Index

Description

Usage

Arguments

Value

Estimate Multivariate Normal Mixture

Description

Usage

Arguments

Details

Value

See Also

Examples

Fit Multivariate Mixture Distribution

Description

Usage

Arguments

Value

Fit Multivariate Normal Distribution

Description

Usage

Arguments

Value

Generate Imputation

Description

Usage

Arguments

Value

Examples

Log likelihood for Fitted GMM

Description

Usage

Arguments

Log likelihood for Fitted MVN Model

Description

Usage

Arguments

Mean for Fitted GMM

Description

Usage

Arguments

Mean for Fitted MVN Model

Description

Usage

Arguments

Mixture Model Class

Description

Slots

Mean Update for Mixture of MVNs with Missingness.

Description

Usage

Arguments

Value