rsem: The main function for robust SEM analysis

Description

This is the function to carry out all analysis.

Usage

rsem(dset, select, EQSmodel, moment=TRUE, varphi=.1, st='i', max.it=1000, 
eqsdata='data.txt', eqsweight='weight.txt', EQSpgm="C:/Progra~1/EQS61/WINEQS.EXE", 
serial="1234")

Value

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

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

Examples

Run this code

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')
 }

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