mxDataWLS {OpenMx}R Documentation

Create MxData Object for Least Squares (WLS, DLS, ULS) Analyses


This function creates a new MxData object of type “ULS” (unweighted least squares), “WLS” (weighted least squares) or “DLS” (diagonally-weighted least squares). The appropriate fit function to include with these models is mxFitFunctionWLS


   mxDataWLS(data, type = "WLS", useMinusTwo = TRUE, returnInverted = TRUE, 
    debug = FALSE, fullWeight = TRUE)



A matrix or data.frame which provides raw data to be used for WLS.


A character string 'WLS' (default), 'DLS', or 'ULS' for weighted, diagonal, or unweighted least squares


Logical indicating whether to use -2LL (default) or -LL.


Logical indicating whether to return the information matrix (default) or the covariance matrix.


Logical to set debugging on or off (default)


Logical determining if the full weight matrix is returned (default). Needed for standard error and quasi-chi-squared calculation.


The mxDataWLS function creates an MxData object, which can be used in MxModel objects. This function takes raw data and returns an MxData object to be used in a model to fit with weighted least squares.

Both Ordinal and continuous data are supported. A combination of these data types also succeeds. Early tests suggested that full 'WLS' might be biased in this joint ordinal and continuous case, whereas 'ULS' and 'DLS' were unbiased. However further investigation revealed that all of these were unbiased when the model is correct, but full 'WLS' is highly sensitive to model misspecification. Full 'WLS' can heavily weight the fourth-order moments of the distribution, so small deviations between the observed fourth-order moments and those implied by the model can lead to poor estimates.


Returns a new MxData object.


The OpenMx User's guide can be found at

See Also

mxFitFunctionWLS. MxData for the S4 class created by mxData. matrix and data.frame for objects which may be entered as arguments in the ‘observed’ slot. More information about the OpenMx package may be found here.


# Create and fit a model using mxMatrix, mxAlgebra, mxExpectationNormal, and mxFitFunctionWLS


# Simulate some data

x=rnorm(1000, mean=0, sd=1)
y= 0.5*x + rnorm(1000, mean=0, sd=1)
tmpFrame <- data.frame(x, y)
tmpNames <- names(tmpFrame)
wdata <- mxDataWLS(tmpFrame)

# Define the matrices

S <- mxMatrix(type = "Full", nrow = 2, ncol = 2, values=c(1,0,0,1), 
              free=c(TRUE,FALSE,FALSE,TRUE), labels=c("Vx", NA, NA, "Vy"), name = "S")
A <- mxMatrix(type = "Full", nrow = 2, ncol = 2, values=c(0,1,0,0), 
              free=c(FALSE,TRUE,FALSE,FALSE), labels=c(NA, "b", NA, NA), name = "A")
I <- mxMatrix(type="Iden", nrow=2, ncol=2, name="I")

# Define the expectation

expCov <- mxAlgebra(solve(I-A) %*% S %*% t(solve(I-A)), name="expCov")
expFunction <- mxExpectationNormal(covariance="expCov", dimnames=tmpNames)

# Choose a fit function

fitFunction <- mxFitFunctionWLS()

# Define the model

tmpModel <- mxModel(model="exampleModel", S, A, I, expCov, expFunction, fitFunction, 

# Fit the model and print a summary

tmpModelOut <- mxRun(tmpModel)

[Package OpenMx version 2.7.9 Index]