The MxNonNullData class has the following slots: rcl{
name - The name of the object
observed - Either a matrix or a data frame
vector - A vector for means, or NA if missing
type - Either 'raw', 'cov', or 'cor'
numObs - The number of oberservations
}
The 'name' slot is the name of the MxData object.
The observed slot is used to contain data, either as a matrix or as a data frame. Use of the data in this slot by other functions depends on the value of the 'type' slot. When 'type' is equal to 'cov' or 'cor', the data input into the 'matrix' slot should be a symmetric matrix or data frame.
The 'vector' slot is used to contain a vector of numeric values, which is used as a vector of means for MxData objects with 'type' equal to 'cov' or 'cor'. This slot may be used in estimation using the mxFitFunctionML function.
The 'type' slot may take one of four supported values:
[object Object],[object Object],[object Object]
The 'numObs' slot describes the number of observations in the data. If 'type' equals 'raw', then 'numObs' is automatically populated as the number of rows in the matrix or data frame in the observed slot. If 'type' equals 'cov' or 'cor', then this slot must be input using the 'numObs' argument in the mxData function when the MxData argument is created.
MxData objects may not be included in MxAlgebra objects or use the mxFitFunctionAlgebra function. If these capabilities are desired, data should be appropriately input or transformed using the mxMatrix and mxAlgebra functions.
While column names are stored in the observed slot of MxData objects, these names are not recognized as variable names in MxPath objects. Variable names must be specified using the 'manifestVars' argument of the mxModel function prior to use in MxPath objects.
The mxData function does not currently place restrictions on the size, shape, or symmetry of matrices input into the observed argument. While it is possible to specify MxData objects as covariance or correlation matrices that do not have the properties commonly associated with these matrices, failure to correctly specify these matrices will likely lead to problems in model estimation.