MBESS (version 4.1.0)

ss.aipe.sem.path: Sample size planning for SEM targeted effects

Description

Plan sample size for structural equation models so that the confidence intervals for the model parameters of interest are sufficiently narrow

Usage

ss.aipe.sem.path(model, Sigma, desired.width, which.path, 
conf.level = 0.95, assurance = NULL, ...)

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 the sem package; see sem and specify.model for detailed documentation about model specifications in the RAM notation.

Sigma

estimated population covariance matrix of the manifest variables

desired.width

desired confidence interval width for the model parameter of interest

which.path

the name of the model parameter of interest, presented in double quotation marks

conf.level

confidence level (i.e., 1- Type I error rate)

assurance

the assurance that the confidence interval obtained in a particular study will be no wider than desired (must be NULL or a value between 0.50 and 1)

...

allows one to potentially include parameter values for inner functions

Value

Details

This function implements the sample size planning methods proposed in Lai and Kelley (2010). It depends on the function sem in the sem package to calculate the expected information matrix, and uses the same notation to specify SEM models as does sem. Please refer to sem for more detailed documentations about model specification, the RAM notation, and model fitting techniques. 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; theta.2.Sigma.theta; ss.aipe.sem.path.sensitiv

Examples

## Not run: 
# # Suppose the model of interest is Model 2 in the simulation study 
# # in Lai and Kelley (2010), and the goal is to obtain a 95% confidence 
# # interval for 'beta21' no wider than 0.3. The necessary sample size 
# # can be calculated as follows.
# 
# library(sem)
# 
# # specify a model object in the RAM notation
# model.2<-specifyModel()
# 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
# 
# # one way to specify the population covariance matrix is to first 
# # specify path coefficients and then calcualte the model-implied 
# # covariance matrix
# 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
# # thus 'Sigma.theta' is the input covariance matrix for sample size 
# # planning procedure.
# 
# # the necessary sample size can be calculated as follows.
# # ss.aipe.sem.path(model=model.2, Sigma=Sigma.theta, 
# # desired.width=0.3, which.path="beta21")
# ## End(Not run)