mvnX {mclust} | R Documentation |
Computes the mean, covariance, and loglikelihood from fitting a single Gaussian (univariate or multivariate normal).
mvnX(data, prior = NULL, warn = NULL, ...) mvnXII(data, prior = NULL, warn = NULL, ...) mvnXXI(data, prior = NULL, warn = NULL, ...) mvnXXX(data, prior = NULL, warn = NULL, ...)
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 set in |
... |
Catches unused arguments in indirect or list calls via |
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 including the following components:
modelName |
A character string identifying the model (same as the input argument). |
parameters |
|
loglik |
The log likelihood for the data in the mixture model. |
Attributes: |
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C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
C. Fraley and A. E. Raftery (2006). MCLUST Version 3: An R Package for Normal Mixture Modeling and Model-Based Clustering, Technical Report, Department of Statistics, University of Washington.
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)