Preprocessor for Multivariate Normal Model Routines

DESCRIPTION:

Sorts and groups the data for subsequent analysis by a multivariate normal model routine which handles missing values using EM or data augmentation algorithms.

USAGE:

preGauss(data, subset) 

REQUIRED ARGUMENTS:

data
a data frame or matrix containing the raw data. When a data frame is input, the model is specified by the numeric variables. When the input object is a matrix, all variables are included in the model.

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.

VALUE:

an object of class "preGauss"; see for details.

DETAILS:

This routine performs the preprocessing required before a data set can be analyzed using the data augmentation or EM algorithms. In repeated calls to the data augmentation, EM, or impute routines, performing this preprocessing once can result in a significant speed up of the computations.

SEE ALSO:

, , , .

EXAMPLES:

cholesterol.pre <- preGauss(data = cholesterol)