MBESS (version 4.1.0)

theta.2.Sigma.theta: Compute the model-implied covariance matrix of an SEM model

Description

Obtain the model-implied covariance matrix of manifest variables given a structural equation model and its model parameters

Usage

theta.2.Sigma.theta(model, theta, latent.vars)

Arguments

model

an RAM (reticular action model; e.g., McArdle & McDonald, 1984) specification of a structural equation model, and should be of class mod. The model is specified in the same manner as does the sem package; see sem and specify.model for detailed documentations about model specifications in the RAM notation.

theta

a vector containing the model parameters. The names of the elements in theta must be the same as the names of the model parameters specified in model.

latent.vars

a vector containing the names of the latent variables

Value

Details

Part of the codes in this function are adapted from the function sem in the sem R package (Fox, 2006). This function uses the same notation to specify SEM models as does sem. Please refer to sem and the example below for more detailed documentation about model specification and the RAM notation. For technical discussion on how to obtain the model implied covariance matrix in the RAM notation given model parameters, see McArdle and McDonald (1984).

References

Fox, J. (2006). Structural equation modeling with the sem package in R. Structural Equation Modeling, 13, 465--486.

Lai, K., & Kelley, K. (in press). Accuracy in parameter estimation for targeted effects in structural equation modeling: Sample size planning for narrow confidence intervals. Psychological Methods.

McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the reticular action model. British Journal of Mathematical and Statistical Psychology, 37, 234--251.

See Also

sem; specify.model

Examples

## Not run: 
# # to obtain the model implied covariance matrix of Model 2 in the simulation 
# # study in Lai and Kelley (2010), one can use the present function in the 
# # following manner.
# 
# library(sem)
# 
# # specify a model object in the RAM notation
# model.2<-specify.model()
# xi1 -> y1, lambda1, 1
# xi1 -> y2, NA, 1
# xi1 -> y3, lambda2, 1
# xi1 -> y4, lambda3, 0.3
# eta1 -> y4, lambda4, 1
# eta1 -> y5, NA, 1
# eta1 -> y6, lambda5, 1
# eta1 -> y7, lambda6, 0.3
# eta2 -> y6, lambda7, 0.3
# eta2 -> y7, lambda8, 1
# eta2 -> y8, NA, 1
# eta2 -> y9, lambda9, 1
# xi1 -> eta1, gamma11, 0.6
# eta1 -> eta2, beta21, 0.6 
# xi1 <-> xi1, phi11, 0.49
# eta1 <-> eta1, psi11, 0.3136
# eta2 <-> eta2, psi22, 0.3136
# y1 <-> y1, delta1, 0.51
# y2 <-> y2, delta2, 0.51
# y3 <-> y3, delta3, 0.51
# y4 <-> y4, delta4, 0.2895
# y5 <-> y5, delta5, 0.51
# y6 <-> y6, delta6, 0.2895
# y7 <-> y7, delta7, 0.2895
# y8 <-> y8, delta8, 0.51
# y9 <-> y9, delta9, 0.51
# 
# 
# # to inspect the specified model
# model.2
# 
# theta <- c(1, 1, 0.3, 1,1, 0.3, 0.3, 1, 1, 0.6, 0.6,
# 0.49, 0.3136, 0.3136, 0.51, 0.51, 0.51, 0.2895, 0.51, 0.2895, 0.2895, 0.51, 0.51)
# 
# names(theta) <- c("lambda1","lambda2","lambda3",
# "lambda4","lambda5","lambda6","lambda7","lambda8","lambda9",
# "gamma11", "beta21",
# "phi11", "psi11", "psi22", 
# "delta1","delta2","delta3","delta4","delta5","delta6","delta7",
# "delta8","delta9")
# 
# res<-theta.2.Sigma.theta(model=model.2, theta=theta, 
# latent.vars=c("xi1", "eta1","eta2"))
# 
# Sigma.theta <- res$Sigma.theta
# ## End(Not run)