See this link: http://lavaan.ugent.be/tutorial/growth.html
LG.mod.ed <- '
int=~1*ALAQSUM+1*CLAQSUM+1*DLAQSUM
slope=~0*ALAQSUM+1*CLAQSUM+2*DLAQSUM
AYPSTAVG.log~ai*int
AYPSTAVG.log~as*slope
DYPSTAVG.log~bi*int+bs*slope+c*AYPSTAVG.log
ab.int := ai*bi
ab.slope := as*bs
total.int := c-ab.int
total.slope := c-ab.slope
#rndcode.binary~int+slope
'
fit.ed <- growth(LG.mod.ed, data=A1.tm, missing="ml")
summary(fit.ed, fit.measures=TRUE, standardized=TRUE)## lavaan (0.5-22) converged normally after 579 iterations
##
## Used Total
## Number of observations 1133 1150
##
## Number of missing patterns 10
##
## Estimator ML
## Minimum Function Test Statistic 24.125
## Degrees of freedom 5
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 1005.276
## Degrees of freedom 10
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.981
## Tucker-Lewis Index (TLI) 0.962
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9302.672
## Loglikelihood unrestricted model (H1) -9290.610
##
## Number of free parameters 15
## Akaike (AIC) 18635.345
## Bayesian (BIC) 18710.834
## Sample-size adjusted Bayesian (BIC) 18663.190
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.058
## 90 Percent Confidence Interval 0.036 0.082
## P-value RMSEA <= 0.05 0.248
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.035
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## int =~
## ALAQSUM 1.000 6.704 0.771
## CLAQSUM 1.000 6.704 0.707
## DLAQSUM 1.000 6.704 0.732
## slope =~
## ALAQSUM 0.000 0.000 0.000
## CLAQSUM 1.000 0.915 0.097
## DLAQSUM 2.000 1.830 0.200
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## AYPSTAVG.log ~
## int (ai) 0.000 0.001 0.492 0.623 0.003 0.016
## slope (as) 0.015 0.024 0.638 0.524 0.014 0.076
## DYPSTAVG.log ~
## int (bi) 0.003 0.002 1.487 0.137 0.018 0.088
## slope (bs) 0.072 0.048 1.496 0.135 0.066 0.330
## AYPSTAVG. (c) 0.667 0.055 12.222 0.000 0.667 0.608
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## int ~~
## slope -0.047 0.911 -0.051 0.959 -0.008 -0.008
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ALAQSUM 0.000 0.000 0.000
## .CLAQSUM 0.000 0.000 0.000
## .DLAQSUM 0.000 0.000 0.000
## .AYPSTAVG.log 0.000 0.000 0.000
## .DYPSTAVG.log 0.000 0.000 0.000
## int 55.899 0.253 220.748 0.000 8.339 8.339
## slope -2.060 0.141 -14.590 0.000 -2.252 -2.252
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ALAQSUM 30.726 2.347 13.090 0.000 30.726 0.406
## .CLAQSUM 44.196 2.756 16.036 0.000 44.196 0.492
## .DLAQSUM 35.872 3.644 9.843 0.000 35.872 0.427
## .AYPSTAVG.log 0.033 0.002 21.664 0.000 0.033 0.994
## .DYPSTAVG.log 0.020 0.003 6.430 0.000 0.020 0.483
## int 44.939 3.142 14.303 0.000 1.000 1.000
## slope 0.837 0.759 1.102 0.270 1.000 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ab.int 0.000 0.000 0.406 0.685 0.000 0.001
## ab.slope 0.001 0.002 0.496 0.620 0.001 0.025
## total.int 0.667 0.055 12.221 0.000 0.667 0.606
## total.slope 0.666 0.056 11.879 0.000 0.666 0.583