mclust (version 2.1-14)

mvnX: Multivariate Normal Fit

Description

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

Usage

mvnX(data)
mvnXII(data)
mvnXXI(data)
mvnXXX(data)

Arguments

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.

Details

mvnXII computes the best fitting Gaussian with the covariance restricted to be a multiple of the identity. mvnXXI computes the best fitting Gaussian with the covariance restricted to be diagonal. mvnXXX computes the best fitting Gaussian with ellipsoidal (unrestricted) covariance.

See Also

mvn, mstepE

Examples

n <- 1000

set.seed(0)
x <- rnorm(n, mean = -1, sd = 2)
mvnX(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 = "+")
mvnXII(x) 

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