`mvnX.Rd`

Computes the mean, covariance, and log-likelihood 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 given by |

... | 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:

A character string identifying the model (same as the input argument).

`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

*k*th 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.

The log likelihood for the data in the mixture model.

`"WARNING"`

An appropriate warning if problems are
encountered in the computations.

if (FALSE) { 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) }