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
Arguments
dset
A data matrix or a data frame
select
Variables to be seleted for SEM analysis. If omitted, all variables in the data set will be used.
moment
With mean structure. For covariance only, set moment=FALSE.
EQSmodel
The input file for EQS. If omitted, only the first-stage analysis will be conducted.
varphi
Proportion of data to be down-weighted. Default is 0.1.
max.it
Maximum number of iterations for EM. Default is 1000
st
Starting values for EM algorithm. The default is 0 for mean and I for covariance. Alternative, the starting values can be estimated according to MCD.
eqsdata
Data file name used in EQS
eqsweight
File name for weight matrix
EQSpgm
The path to the installed EQS program
serial
The serial no of EQS
Author
Ke-Hai Yuan and Zhiyong Zhang
Details
This function will run the robust analysis and output results.
References
Ke-Hai Yuan and Zhiyong Zhang (2011) Robust Structural Equation Modeling with Missing Data and Auxiliary Variables
if (FALSE) {
## an example## to use eqs, first load the package semdiag library(semdiag)
data(mardiamv25)
analysis<-rsem(mardiamv25, c(1,2,4,5), 'eqsinput.eqs')
}