mxLISRELObjective: Create MxLISRELObjective Object

Description

This function creates a new MxLISRELObjective object.

Usage

mxLISRELObjective(LX=NA, LY=NA, BE=NA, GA=NA, PH=NA, PS=NA, TD=NA, TE=NA, TH=NA,
   TX = NA, TY = NA, KA = NA, AL = NA,
   dimnames = NA, thresholds = NA, vector = FALSE, threshnames = dimnames)

Arguments

An optional character string indicating the name of the 'LX' matrix.

An optional character string indicating the name of the 'LY' matrix.

An optional character string indicating the name of the 'BE' matrix.

An optional character string indicating the name of the 'GA' matrix.

An optional character string indicating the name of the 'PH' matrix.

An optional character string indicating the name of the 'PS' matrix.

An optional character string indicating the name of the 'TD' matrix.

An optional character string indicating the name of the 'TE' matrix.

An optional character string indicating the name of the 'TH' matrix.

An optional character string indicating the name of the 'TX' matrix.

An optional character string indicating the name of the 'TY' matrix.

An optional character string indicating the name of the 'KA' matrix.

An optional character string indicating the name of the 'AL' matrix.

dimnames

An optional character vector that is currently ignored

thresholds

An optional character string indicating the name of the thresholds matrix.

vector

A logical value indicating whether the objective function result is the likelihood vector.

threshnames

An optional character vector to be assigned to the column names of the thresholds matrix.

Value

Returns a new MxLISRELObjective object. MxLISRELObjective objects should be included with models with referenced MxAlgebra, MxData and MxMatrix objects.

Details

Objective functions are functions for which free parameter values are chosen such that the value of the objective function is minimized. The mxLISRELObjective provides maximum likelihood estimates of free parameters in a model of the covariance of a given MxData object. This model is defined by LInear Structual RELations (LISREL; Jreskog & Srbom, 1982, 1996). Arguments 'LX' through 'AL' must refer to MxMatrix objects with the associated properties of their respective matrices in the LISREL modeling approach. The full LISREL specification has 13 matrices and is sometimes called the extended LISREL model. It is defined by the following equations. $$\eta = \alpha + B \eta + \Gamma \xi + \zeta$$ $$y = \tau_y + \Lambda_y \eta + \epsilon$$ $$x = \tau_x + \Lambda_x \xi + \delta$$ The table below is provided as a quick reference to the numerous matrices in LISREL models. Note that NX is the number of manifest exogenous (independent) variables, the number of Xs. NY is the number of manifest endogenous (dependent) variables, the number of Ys. NK is the number of latent exogenous variables, the number of Ksis or Xis. NE is the number of latent endogenous variables, the number of etas.

Matrix	Word	Abbreviation	Dimensions	Expression	Description
$\Lambda_x$	Lambda x	LX	NX x NK		Exogenous Factor Loading Matrix
$\Lambda_y$	Lambda y	LY	NY x NE		Endogenous Factor Loading Matrix
$B$	Beta	BE	NE x NE		Regressions of Latent Endogenous Variables Predicting Endogenous Variables
$\Gamma$	Gamma	GA	NE x NK		Regressions of Latent Exogenous Variables Predicting Endogenous Variables
$\Phi$	Phi	PH	NK x NK	cov($\xi$)	Covariance Matrix of Latent Exogenous Variables
$\Psi$	Psi	PS	NE x NE	cov($\zeta$)	Residual Covariance Matrix of Latent Endogenous Variables
$\Theta_{\delta}$	Theta delta	TD	NX x NX	cov($\delta$)	Residual Covariance Matrix of Manifest Exogenous Variables
$\Theta_{\epsilon}$	Theta epsilon	TE	NY x NY	cov($\epsilon$)	Residual Covariance Matrix of Manifest Endogenous Variables
$\Theta_{\delta \epsilon}$	Theta delta epsilson	TH	NX x NY	cov($\delta, \epsilon$)	Residual Covariance Matrix of Manifest Exogenous with Endogenous Variables
$\tau_x$	tau x	TX	NX x 1		Residual Means of Manifest Exogenous Variables
$\tau_y$	tau y	TY	NY x 1		Residual Means of Manifest Endogenous Variables
$\kappa$	kappa	KA	NK x 1	mean($\xi$)	Means of Latent Exogenous Variables
$\alpha$	alpha	AL	NE x 1		Residual Means of Latent Endogenous Variables

From the extended LISREL model, several submodels can be defined. Subtypes of the LISREL model are defined by setting some of the arguments of the LISREL objective to NA. Note that because the default values of each LISREL matrix is NA, setting a matrix to NA can be accomplished by simply not giving it any other value. The first submodel is the LISREL model without means. $$\eta = B \eta + \Gamma \xi + \zeta$$ $$y = \Lambda_y \eta + \epsilon$$ $$x = \Lambda_x \xi + \delta$$ The LISREL model without means requires 9 matrices: LX, LY, BE, GA, PH, PS, TD, TE, and TH. Hence this LISREL model has TX, TY, KA, and AL as NA. This can be accomplished be leaving these matrices at their default values. The TX, TY, KA, and AL matrices must be specified if either the mxData type is “cov” or “cor” and a means vector is provided, or if the mxData type is “raw”. Otherwise the TX, TY, KA, and AL matrices are ignored and the model without means is estimated. A second submodel involves only endogenous variables. $$\eta = B \eta + \zeta$$ $$y = \Lambda_y \eta + \epsilon$$ The endogenous-only LISREL model requires 4 matrices: LY, BE, PS, and TE. The LX, GA, PH, TD, and TH must be NA in this case. However, means can also be specified, allowing TY and AL if the data are raw or if observed means are provided. Another submodel involves only exogenous variables. $$x = \Lambda_x \xi + \delta$$ The exogenous-model model requires 3 matrices: LX, PH, and TD. The LY, BE, GA, PS, TE, and TH matrices must be NA. However, means can also be specified, allowing TX and KA if the data are raw or if observed means are provided. The model that is run depends on the matrices that are not NA. If all 9 matrices are not NA, then the full model is run. If only the 4 endogenous matrices are not NA, then the endogenous-only model is run. If only the 3 exogenous matrices are not NA, then the exogenous-only model is run. If some endogenous and exogenous matrices are not NA, but not all of them, then appropriate errors are thrown. Means are included in the model whenever their matrices are provided. The MxMatrix objects included as arguments may be of any type, but should have the properties described above. The mxLISRELObjective will not return an error for incorrect specification, but incorrect specification will likely lead to estimation problems or errors in the mxRun function. Like the mxRAMObjective, the mxLISRELObjective evaluates with respect to an MxData object. The MxData object need not be referenced in the mxLISRELObjective function, but must be included in the MxModel object. mxLISRELObjective requires that the 'type' argument in the associated MxData object be equal to 'cov', 'cor', or 'raw'. To evaluate, place MxLISRELObjective objects, the mxData object for which the expected covariance approximates, referenced MxAlgebra and MxMatrix objects, and optional MxBounds and MxConstraint objects in an MxModel object. This model may then be evaluated using the mxRun function. The results of the optimization can be found in the 'output' slot of the resulting model, and may be obtained using the mxEval function.

References

Jreskog, K. G. & Srbom, D. (1996). LISREL 8: User's Reference Guide. Lincolnwood, IL: Scientific Software International. Jreskog, K. G. & Srbom, D. (1982). Recent developments in structural equation modeling. Journal of Marketing Research, 19, 404-416. The OpenMx User's guide can be found at http://openmx.ssri.psu.edu/documentation.

Examples

Run this code

   
   #####------------------------------#####
   ##### Factor Model
   mLX <- mxMatrix("Full", values=c(.5, .6, .8, rep(0, 6), .4, .7, .5),
      name="LX", nrow=6, ncol=2,
      free=c(TRUE,TRUE,TRUE,rep(FALSE, 6),TRUE,TRUE,TRUE))
   mTD <- mxMatrix("Diag", values=c(rep(.2, 6)), name="TD", nrow=6, ncol=6,
      free=TRUE)
   mPH <- mxMatrix("Symm", values=c(1, .3, 1), name="PH", nrow=2, ncol=2,
      free=c(FALSE, TRUE, FALSE))
   
   # Create a LISREL objective with LX, TD, and PH matrix names
   objective <- mxLISRELObjective(LX="LX", TD="TD", PH="PH")
   
   testModel <- mxModel(model="testModel", mLX, mTD, mPH, objective)

Run the code above in your browser using DataLab