See this link: http://lavaan.ugent.be/tutorial/growth.html
### Model
LG.mod <- '
intercept=~1*ALAQSUM+1*CLAQSUM+1*DLAQSUM
slope=~0*ALAQSUM+1*CLAQSUM+2*DLAQSUM
intercept~AAUDITTot.log
slope~AAUDITTot.log
'
fit <- growth(LG.mod, data=A1.tm, missing="ml")
summary(fit, fit.measures=TRUE, standardized=TRUE)## lavaan (0.5-22) converged normally after 113 iterations
##
## Used Total
## Number of observations 1125 1142
##
## Number of missing patterns 8
##
## Estimator ML
## Minimum Function Test Statistic 0.264
## Degrees of freedom 2
## P-value (Chi-square) 0.876
##
## Model test baseline model:
##
## Minimum Function Test Statistic 747.639
## Degrees of freedom 6
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.007
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -11239.280
## Loglikelihood unrestricted model (H1) -11239.148
##
## Number of free parameters 10
## Akaike (AIC) 22498.560
## Bayesian (BIC) 22548.816
## Sample-size adjusted Bayesian (BIC) 22517.053
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent Confidence Interval 0.000 0.029
## P-value RMSEA <= 0.05 0.991
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.003
##
## Parameter Estimates:
##
## Information Observed
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intercept =~
## ALAQSUM 1.000 7.772 0.923
## CLAQSUM 1.000 7.772 0.796
## DLAQSUM 1.000 7.772 0.902
## slope =~
## ALAQSUM 0.000 0.000 0.000
## CLAQSUM 1.000 3.820 0.391
## DLAQSUM 2.000 7.639 0.886
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## intercept ~
## AAUDITTot.log 0.443 0.279 1.584 0.113 0.057 0.052
## slope ~
## AAUDITTot.log -0.159 0.171 -0.932 0.351 -0.042 -0.038
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .intercept ~~
## .slope -12.511 2.477 -5.051 0.000 -0.422 -0.422
##
## 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
## .intercept 55.433 0.352 157.383 0.000 7.132 7.132
## .slope -1.884 0.205 -9.206 0.000 -0.493 -0.493
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ALAQSUM 10.536 4.168 2.528 0.011 10.536 0.149
## .CLAQSUM 45.479 2.822 16.114 0.000 45.479 0.477
## .DLAQSUM 5.770 4.315 1.337 0.181 5.770 0.078
## .intercept 60.247 4.861 12.395 0.000 0.997 0.997
## .slope 14.568 2.075 7.019 0.000 0.999 0.999 ##Add Clustering (do not use)
# survey.design <- svydesign(ids=~SCHOOL,prob=~1,data=A1.t,missing="ml")
# survey.fit <- lavaan.survey(lavaan.fit=fit,survey.design = survey.design)
# summary(survey.fit,fit.measures=TRUE,standardized=TRUE,modindices=TRUE)
##Residual correlation
#getCor(fit) #Does not apply to latent growth model