mxData

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This function creates a new MxData object for use in an mxModel.

If an mxModel does not have mxData, it will inherit the data of its parent.

Usage

mxData(observed, type, means = NA, numObs = NA)

Arguments

observed A matrix or data.frame which provides data to the MxData object.
type A character string defining the type of data in the ‘observed’ argument. Must be one of “raw”, “cov”, “cor”, or “sscp”.
means An optional vector of means for use when ‘type’ is “cov”, or “cor”.
numObs The number of observations in the data supplied in the ‘observed’ argument. Required unless ‘type’ equals “raw”.

Details

The mxData function creates MxData objects, which can be used as arguments in MxModel objects. The ‘observed’ argument may take either a data frame or a matrix, which is then described with the ‘type’ argument. Data types describe compatibility and usage with objective functions in MxModel objects. Four different data types are supported:

raw

The contents of the ‘observed’ argument are treated as raw data. Missing values are permitted and must be designated as the system missing value. The ‘means’ and ‘numObs’ arguments cannot be specified, as the ‘means’ argument is not relevant and the ‘numObs’ argument is automatically populated with the number of rows in the data. Data of this type must use the mxFIMLObjective function as its objective function in MxModel objects, which deals with covariance estimation under full-information maximum likelihood.

cov

The contents of the ‘observed’ argument are treated as a covariance matrix. The ‘means’ argument is not required, but may be included for estimations involving means. The ‘numObs’ argument is required, which should reflect the number of observations or rows in the data described by the covariance matrix. Data of this type may use the mxMLObjective, or mxRAMObjective functions, depending on the specified model.

cor

The contents of the ‘observed’ argument are treated as a correlation matrix. The ‘means’ argument is not required, but may be included for estimations involving means. The ‘numObs’ argument is required, which should reflect the number of observations or rows in the data described by the covariance matrix. Data of this type may use the mxMLObjective, or mxRAMObjective functions, depending on the specified model.

sscp

The contents of the ‘observed’ argument are treated as a sums-of-squares and cross-products matrix. The ‘means’ argument is not used. The ‘numObs’ argument is required, which should reflect the number of observations or rows in the data described by the covariance matrix. Data of this type may use the mxMLObjective, or mxRAMObjective functions, depending on the specified model.

MxData objects may not be included in MxAlgebra objects or use the mxAlgebraObjective 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, correlation or sscp 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.

OpenMx uses the names of variables to map them onto the objective functions and other elements associated with your model. For data.frames, ensure you have set the names(). For matrices set names using, for instance, row.names=c("your", "columns"). Covariance cor and sscp matrices need to have both the row and column names set and these must be identical, for instance by using dimnames=list(varNames, varNames).

Notes

If an mxModel does not have mxData of its own, it will inherit the data of its parent.

See Also

Examples

# Create a covariance matrix
covMatrix <- matrix( c(0.77642931, 0.39590663, 0.39590663, 0.49115615), nrow = 2, ncol = 2, byrow = TRUE)

# Create an MxData object including that covariance matrix
data <- mxData(covMatrix, 'cov', numObs = 100)
model <- mxModel(data)