rsem.print | R Documentation |
Organize the output for Lavaan with robust s.e. and test statistics. Modified from the print function of Lavaan.
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 indices |
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)
## }
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