# 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'
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
Group 1
Variances:
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
Group 2
Variances:
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
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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