mvnX {mclust} | R Documentation |
Computes the mean, covariance, and loglikelihood from fitting a single MVN or Gaussian.
mvnX(data) mvnXII(data) mvnXXI(data) mvnXXX(data)
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. |
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.
A list of including the parameters of the Gaussian model best fitting the data, and the corresponding loglikelihood for the data under the model.
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.
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)