Last data update: 2014.03.03
R: Data analysis using the model specification
Data analysis using the model specification
Description
Data analysis using the model specification (linkS4class{SimSem}) or the mx model object (MxModel). Data will be multiply imputed if the miss argument is specified.
Usage
analyze(model, data, package="lavaan", miss=NULL, aux=NULL, group = NULL,
mxMixture = FALSE, ...)
Arguments
model
The simsem model template (linkS4class{SimSem}) or the mx model object (MxModel)
data
The target dataset
package
The package used in data analysis. Currently, only lavaan package can be used.
miss
The missing object with the specification of auxiliary variable or the specification for the multiple imputation.
aux
List of auxiliary variables
group
A group variable. This argument is applicable only when the model argument is a MxModel object.
mxMixture
A logical whether to the analysis model is a mixture model. This argument is applicable when MxModel is used in the model argument only.
...
Additional arguments in the lavaan function
Value
The lavaan object containing the output
Author(s)
Patrick Miller (University of Notre Dame; pmille13@nd.edu ),
Sunthud Pornprasertmanit (psunthud@gmail.com )
See Also
Note that users can use functions provided by lavaan package (lavaan, cfa, sem, or growth) if they wish to analyze data by lavaan directly.
For the OpenMx result, users may request standardized measures and additional fit indices by the standardizeMx and fitMeasuresMx functions in the semTools package.
Examples
loading <- matrix(0, 6, 2)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
LY <- bind(loading, 0.7)
latent.cor <- matrix(NA, 2, 2)
diag(latent.cor) <- 1
RPS <- binds(latent.cor, 0.5)
RTE <- binds(diag(6))
VY <- bind(rep(NA,6),2)
CFA.Model <- model(LY = LY, RPS = RPS, RTE = RTE, modelType = "CFA")
dat <- generate(CFA.Model,200)
out <- analyze(CFA.Model,dat)
Results
providing 
.