mclust: mvn – R documentation

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mvn

Univariate or Multivariate Normal Fit

Description

Computes the mean, covariance, and log-likelihood from fitting a single Gaussian to given data (univariate or multivariate normal).

Usage

mvn( modelName, data, prior = NULL, warn = NULL, ...)

Arguments

`modelName`	A character string representing a model name. This can be either `"Spherical"`, `"Diagonal"`, or `"Ellipsoidal"` or else `"X"` for one-dimensional data, `"XII"` for a spherical Gaussian, `"XXI"` for a diagonal Gaussian `"XXX"` for a general ellipsoidal Gaussian
`data`	A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
`prior`	Specification of a conjugate prior on the means and variances. The default assumes no prior.
`warn`	A logical value indicating whether or not a warning should be issued whenever a singularity is encountered. The default is given by `mclust.options("warn")`.
`...`	Catches unused arguments in indirect or list calls via `do.call`.

Value

A list including the following components:

`modelName`	A character string identifying the model (same as the input argument).
`parameters`	`mean` The mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model. `variance` A list of variance parameters for the model. The components of this list depend on the model specification. See the help file for `mclustVariance` for details.
`loglik`	The log likelihood for the data in the mixture model.
`Attributes:`	`"WARNING"` An appropriate warning if problems are encountered in the computations.

Examples

n <- 1000

set.seed(0)
x <- rnorm(n, mean = -1, sd = 2)
mvn(modelName = "X", x) 

mu <- c(-1, 0, 1)

set.seed(0)
x <- sweep(matrix(rnorm(n*3), n, 3) %*% (2*diag(3)), 
           MARGIN = 2, STATS = mu, FUN = "+")
mvn(modelName = "XII", x) 
mvn(modelName = "Spherical", x) 

set.seed(0)
x <- sweep(matrix(rnorm(n*3), n, 3) %*% diag(1:3), 
           MARGIN = 2, STATS = mu, FUN = "+")
mvn(modelName = "XXI", x)
mvn(modelName = "Diagonal", x)

Sigma <- matrix(c(9,-4,1,-4,9,4,1,4,9), 3, 3)
set.seed(0)
x <- sweep(matrix(rnorm(n*3), n, 3) %*% chol(Sigma), 
           MARGIN = 2, STATS = mu, FUN = "+")
mvn(modelName = "XXX", x) 
mvn(modelName = "Ellipsoidal", x)

mclust

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

v5.4.10

GPL (>= 2)

Authors

Chris Fraley [aut], Adrian E. Raftery [aut] (<https://orcid.org/0000-0002-6589-301X>), Luca Scrucca [aut, cre] (<https://orcid.org/0000-0003-3826-0484>), Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>), Michael Fop [ctb] (<https://orcid.org/0000-0003-3936-2757>)

Initial release

2022-05-20

mvn