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.