R: Univariate or Multivariate Normal Fit

mvnX {mclust} | R Documentation

## Univariate or Multivariate Normal Fit

### Description

Computes the mean, covariance, and log-likelihood from fitting a single Gaussian (univariate or multivariate normal).

### Usage

    mvnX(data, prior = NULL, warn = NULL, ...)
    mvnXII(data, prior = NULL, warn = NULL, ...)
    mvnXXI(data, prior = NULL, warn = NULL, ...)
    mvnXXX(data, prior = NULL, warn = NULL, ...)

### 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.
`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 `mclust.options("warn")`.
`...` | Catches unused arguments in indirect or list calls via `do.call`.

### 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.

### Value

A list including the following components:

`modelName` | A character string identifying the model (same as the input argument).
`parameters` | `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 kth 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.
`loglik` | The log likelihood for the data in the mixture model.
`Attributes:` | `"WARNING"` An appropriate warning if problems are encountered in the computations.

### See Also

`mvn`, `mstepE`

### Examples

    ## Not run:
    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)

    ## End(Not run)

[Package mclust version 5.4.5 Index]