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lab:projects:14run_eqs_from_r_for_data_analysis:index

## Run EQS from R for data analysis

### A working example

This example estimates a two factor model with mean structure.

1. Prepare data. We put data into a matrix with covariance matrix followed by means
2.845 0.759 0.227 0.76 0.876 -0.162
0.759 1.041 0.415 0.208 0.198 -0.1
0.227 0.415 1.433 0.006 0.192 0.248
0.76 0.208 0.006 1.051 0.576 0.084
0.876 0.198 0.192 0.576 1.073 0.236
-0.162 -0.1 0.248 0.084 0.236 0.81
0.305 0.096 0.072 0.183 0.203 0.122

The data are saved in a file called runr.dat.

2. Build a working EQS input file. For example,
/TITLE
SIMULATED CONFIRMATORY FACTOR ANALYSIS EXAMPLE (EXAMPLE IN EQS MANUAL P.117)
RAW SCORES IN BENTLER (1985, P.105)
DEFAULT START VALUES
/SPECIFICATIONS
CASES = 50; VARIABLES = 6; ME = ML, ROBUST;
MAT=COV; data='runr.dat'; ANALYSIS=MOMENT;
/EQUATIONS
V1 =*V999 + *F1      + E1;
V2 =*V999 + *F1      + E2;
V3 =*V999 + *F1      + E3;
V4 =*V999 +      *F2 + E4;
V5 =*V999 +      *F2 + E5;
V6 =*V999 +      *F2 + E6;
/VARIANCES
F1 TO F2 = 1;
E1 TO E6 = *;
/COVARIANCE
F1,F2 = *;
/MEANS
/OUTPUT
CODEBOOK;
DATA='runr.ETS';
PARAMETER ESTIMATES;
STANDARD ERRORS;
LISTING;
/END


Note that:

• The data file is specified by MAT=COV; data='runr.dat';
• The means are provided by /MEANS in the model part.
• We need to specify the output section in order to read results into R later on.
3. To run the analysis using REQS, the codes are
write.table(rbind(cov.test, m.test),'runr.dat', row.names=F, col.names=F)
res <- run.eqs(EQSpgm = '"C:/Program Files/EQS61/WINEQS"',
EQSmodel = "c:/eqs61/examples/runr.eqs", serial = 1234)
4. To view the results, look at each component of res.
> names(res)
[1] "model.info"  "pval"        "fit.indices" "model.desc"  "Phi"
[6] "Gamma"       "Beta"        "par.table"   "sample.cov"  "sigma.hat"
[11] "inv.infmat"  "rinv.infmat" "cinv.infmat" "derivatives" "moment4"
[16] "ssolution"   "Rsquared"    "fac.means"   "var.desc"    "indstd"
[21] "depstd"