rsem.print: Orgnize the output for Lavaan with roubust s.e. and test...
In rsem: Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

Description Usage Arguments Details Value Author(s) References See Also Examples

Orgnize the output for Lavaan with roubust s.e. and test statistics. Modified from the print function of Lavaan.

1	rsem.print(object, robust.se, robust.fit, estimates=TRUE, fit.measures=FALSE, standardized=FALSE, rsquare=FALSE, std.nox=FALSE, modindices=FALSE)

`object`	Output from lavaan analysis, such as growth, factor, sem functions.
`robust.se`	Robust standard error from the function rsem.se
`robust.fit`	Robust fit statistics from the function rsem.fit
`estimates`	Show parameter estimates
`fit.measures`	Show fit statistics of lavaan (no need for it)
`standardized`	standardized coefficients
`rsquare`	R square for dependent variables.
`std.nox`	to add
`modindices`	Modification indeces

This function will run the robust analysis and output results.

If EQSmodel is not supplied

`sem`	Information for SEM analysis including estimated means, covariance matrix and their sandwich type covariance matrix in the order of mean first and then covariance matrix.
`misinfo`	Information related to missing data pattern
`em`	Results from expectation robust algorithm
`ascov`	Covariance matrix

If EQSmodel is supplied,

sem

Information for SEM analysis including estimated means, covariance matrix and their sandwich type covariance matrix according to the requirement of EQS.

In addition, the following model parameters are from EQS

`fit.stat`	Fit indices and associated p-values
`para`	Parameter estimates
`eqs`	All information from REQS

Ke-Hai Yuan and Zhiyong Zhang

Ke-Hai Yuan and Zhiyong Zhang (2011) Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

rsem.pattern, rsem.emmusig, rsem.Ascov

##\dontrun{
## an example
 data(mardiamv25)
 names(mardiamv25)<-paste('V', 1:5, sep='')
 
 fa.model<-'f1 =~ V1 + V2
f2 =~ V4 + V5 
f1 ~ 1
f2 ~ 1
V1 ~0*1
V2 ~0*1
V4 ~0*1
V5 ~0*1'

pat<-rsem.pattern(mardiamv25)

phi<-0.1
musig<-rsem.emmusig(pat, varphi=phi)

res.lavaan<-sem(fa.model, sample.cov=musig$sigma, sample.mean=musig$mu, sample.nobs=88,mimic='EQS')

ascov<-rsem.Ascov(pat, musig, varphi=phi)

robust.se<-rsem.se(res.lavaan, ascov$Gamma)

robust.fit <- rsem.fit(res.lavaan, ascov$Gamma, musig)

rsem.print(res.lavaan, robust.se, robust.fit)
## }