# In multiple group LGC model, how do I constrain residual variances of mani. vars within each group?

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### Craig Van Pay

Jun 10, 2019, 2:06:17 PM6/10/19
to lavaan
Hello,

I am using Lavaan 0.6-4.1411 and building up a LGC, and I want to constrain residual variance within each group across the manifest variables, but I cannot figure out how to do so. When using group.equal in my growth( ) function, it constrains them, but gives each group the same estimate. Can I have Lavaan estimate constrained residuals for each group?

Model specification:

`# mean latent intercept and constrained residual variancesgrowth.model1 <- '       # intercept         i =~ 1*TVIPR_1 + 1*TVIPR_2 + 1*TVIPR_3         i~~0*i       # residual variances         TVIPR_1~~r*TVIPR_1         TVIPR_2~~r*TVIPR_2         TVIPR_3~~r*TVIPR_3'`

Output without group.equal( ):

`Group 1`
```Variances:
Estimate  Std.Err  z-value  P(>|z|)
i                 0.000
.TVIPR_1    (r)  238.593   18.254   13.071    0.000
.TVIPR_2    (r)  238.593   18.254   13.071    0.000
.TVIPR_3    (r)  238.593   18.254   13.071    0.000Group 2Variances:
Estimate  Std.Err  z-value  P(>|z|)
i                 0.000
.TVIPR_1         134.251   17.666    7.599    0.000
.TVIPR_2         233.083   67.010    3.478    0.001
.TVIPR_3         440.732   41.172   10.705    0.000```

Output with group.equal( ):

```Group 1Variances: Estimate Std.Err z-value P(>|z|) i 0.000 .TVIPR_1 (r) 247.827 15.819 15.666 0.000 .TVIPR_2 (r) 247.827 15.819 15.666 0.000 .TVIPR_3 (r) 247.827 15.819 15.666 0.000Group 2Variances: Estimate Std.Err z-value P(>|z|) i 0.000 .TVIPR_1 (r) 247.827 15.819 15.666 0.000 .TVIPR_2 (r) 247.827 15.819 15.666 0.000 .TVIPR_3 (r) 247.827 15.819 15.666 0.000```

Notice how it only constrains the first group, and then both at the same.

Thanks!

Craig

### Christopher David Desjardins

Jun 10, 2019, 2:12:47 PM6/10/19
to lavaan

pre-multiply:

``````Demo.growth\$group <- rep(c("1", "2"), each = 200)
model.syntax <- '
# intercept and slope with fixed coefficients
i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4
i ~~ 0*i

# time-varying covariates
t1 ~~ c("p1", "p2")*t1
t2 ~~ c("p1", "p2")*t2
t3 ~~ c("p1", "p2")*t3
t4 ~~ c("p1", "p2")*t4
'
fit <- growth(model.syntax, group = "group", data=Demo.growth)
summary(fit)
``````

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