mclust (version 2.1-14)

mvn: Multivariate Normal Fit

Description

Computes the mean, covariance, and loglikelihood from fitting a single MVN or Gaussian to given data.

Usage

mvn( modelName, data)

Arguments

modelName

A character string representing a model name. This can be either "Spherical", "Diagonal", or "Ellipsoidal" or an MCLUST-style model name: "E", "V", "X" (one-dimensional) "EII", "VII", "XII" (spherical) "EE

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.

Value

• A list of including the parameters of the Gaussian model best fitting the data, and the corresponding loglikelihood for the data under the model.

References

C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust. C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

See Also

mvnX, mvnXII, mvnXXI, mvnXXX, mstep

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)