OpenMx (version 2.3.1)

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

Description

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

Usage

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

Arguments

data

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

type

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

useMinusTwo

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

returnInverted

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

debug

Logical to set debugging on or off (default)

fullWeight

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

Value

• Returns a new MxData object.

Details

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 succeeds without error, but when using 'WLS' or 'DLS' the answers appear incorrect. The 'ULS' estimates for joint ordinal and continuous data appear accurate. Consequently, do not use this function for joint problems unless type='ULS'.

References

The OpenMx User's guide can be found at http://openmx.psyc.virginia.edu/documentation.

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.

Examples

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

library(OpenMx)

# 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, 
                    wdata)

# Fit the model and print a summary

tmpModelOut <- mxRun(tmpModel)
summary(tmpModelOut)