metaSEM (version 1.4.0)

create.mxModel: 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,
               replace.constraints=FALSE, mxModel.Args=NULL,
               run=TRUE, silent=TRUE, ...)

Value

An object of class mxRAMmodel

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 named starting values of the free parameters, e.g., list(a=1, b=2)

replace.constraints

Logical. If TRUE, the parameters on the left hand side will be replaced by the constraints on the right hand side. That is, the parameters on the left hand side are no longer parameters in the model.

mxModel.Args

A list of arguments passed to mxModel.

run

Logical. If FALSE, only return the mx model without running the analysis.

silent

Logical. An argument is passed to either mxRun or mxTryHard

...

Further arguments will be passed to either mxRun or mxTryHard

Author

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

Examples

# \donttest{
## 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)
# }