Multivariate Normal Models for Complete Data

DESCRIPTION:

Compute parameter estimates for data with no missing values in multivariate normal models.

USAGE:

completeGauss(data, subset, prior = <<see below>>, 
    start = <<see below>>, control = emGauss.control())  

REQUIRED ARGUMENTS:

data
a data frame or matrix containing the raw data. When a data frame is input, the variables are specified as all numeric variables. When a matrix is input, all variables are used.

OPTIONAL ARGUMENTS:

subset
expression specifying which rows of the data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of rows), a numeric vector indicating the observation numbers to be included, or a character vector of the row names to be included. All observations are included by default. If data is a data frame, this expression may use variables in the data frame.
prior
an object of class "priorGauss" giving the hyperparameters of the prior distribution. Routine priorGauss is used to create the class "priorGauss" object. Alternatively, the character strings "ml" (for no prior, i.e., maximum likelihood estimation), "noninformative" (for a noninformative prior), or "ridge" (for the default ridge prior) may be used. Pattern matching means that only the first character in the string is required. See for details.

The default value is a noninformative prior.
start
starting values of the parameters. This argument is not used by function completeGauss, but is included to conform with other missing data functions.
control
a list of parameters used to control the EM algorithm. This argument is not used by completeGauss, but is included to conform with other missing data functions.

VALUE:

an object of class "missmodel" is returned; see for details. In the class "missmodel" object returned by completeGauss, the paramIter component contains one or more rows of parameter estimates, and the algorithm element contains an object of class "em".

DETAILS:

The completeGauss function computes Bayes estimates of the parameters in a multivariate normal model.

REFERENCES:

Schafer, J. L. (1997), Analysis of Incomplete Multivariate Data, Chapman & Hall, London.

SEE ALSO:

, , , , , , , , .

EXAMPLES:

completeGauss(data = na.omit(cholesterol))