create.mxModel: Create an mxModel

In metaSEM: Meta-Analysis using Structural Equation Modeling

Create an mxModel

Description

It creates a mxModel from a RAM object.

Usage

create.mxModel(model.name="mxModel", RAM=NULL, data=NULL,
               Cov=NULL, means=NULL, numObs,
               intervals.type = c("z", "LB"), startvalues=NULL, 
               mxModel.Args=NULL, run=TRUE, mxTryHard=FALSE,
               silent=TRUE, ...)

Arguments

model.name	A string for the model name in mxModel.
RAM	A RAM object including a list of matrices of the model returned from lavaan2RAM.
data	A data frame or matrix of data.
Cov	A covariance matrix may also be used if data==NULL.
means	A named vector of means (options) if Cov is used.
numObs	If Cov is used, a sample size must be provided.
intervals.type	Either z (default if missing) or LB. If it is z, it calculates the 95% confidence intervals (CIs) based on the estimated standard error. If it is LB, it calculates the 95% likelihood-based CIs on the parameter estimates.
startvalues	A list of starting values for the free parameters.
mxModel.Args	A list of arguments passed to mxModel.
run	Logical. If FALSE, only return the mx model without running the analysis.
mxTryHard	If TRUE, call mxTryHard to run the analysis. If FALSE, call mxRun to run the analysis.
silent	Logical. An argument is passed to either mxRun or mxTryHard
...	Further arguments will be passed to either mxRun or mxTryHard

Value

An object of class mxModel

Author(s)

Mike W.-L. Cheung <[e-mail]>

Examples

## Not run: 
## Generate data
set.seed(100)
n <- 100
x <- rnorm(n)
y <- 0.5*x + rnorm(n, mean=0, sd=sqrt(1-0.5^2))
my.df <- data.frame(y=y, x=x)

## A regression model
model <- "y ~ x   # Regress y on x
          y ~ 1   # Intercept of y
          x ~ 1   # Mean of x"
plot(model)

RAM <- lavaan2RAM(model, obs.variables=c("y", "x"))

my.fit <- create.mxModel(RAM=RAM, data=my.df)
summary(my.fit)

## A meta-analysis
model <- "f =~ 1*yi
          f ~ mu*1          ## Average effect
          f ~~ tau2*f       ## Heterogeneity variance
          yi ~~ data.vi*yi  ## Known sampling variance"
plot(model)

## Do not standardize the latent variable (f): std.lv=FALSE 
RAM <- lavaan2RAM(model, obs.variables="yi", std.lv=FALSE)

## Use likelihood-based CI 
my.fit <- create.mxModel(RAM=RAM, data=Hox02, intervals="LB")
summary(my.fit)

## End(Not run)

metaSEM documentation built on Aug. 10, 2023, 1:09 a.m.