library(lcsm)
#> 
#> ── This is lcsm 0.1.6 ──────────────────────────────────────────────────────────
#> ℹ Please report any issues or ideas at:
#> ℹ https://github.com/milanwiedemann/lcsm/issues
library(lavaan)
#> This is lavaan 0.6-9
#> lavaan is FREE software! Please report any bugs.

The main underlying functions to extract parameters and fit statistics come from the broom package: broom::tidy() and broom::glance(). The functions extract_param() and extract_fit() offer some tools that I find helpful when running LCSMs in R, for example:

Create univariate models

# First fit some latent change score models

# No change model
uni_lcsm_01 <- fit_uni_lcsm(data = data_uni_lcsm, 
                            var = c("x1", "x2", "x3", "x4", "x5"),
                            model = list(alpha_constant = FALSE, 
                                         beta = FALSE, 
                                         phi = FALSE))
#> Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
#>   239
# Constant change only model
uni_lcsm_02 <- fit_uni_lcsm(data = data_uni_lcsm, 
                            var = c("x1", "x2", "x3", "x4", "x5"),
                            model = list(alpha_constant = TRUE, 
                                         beta = FALSE, 
                                         phi = FALSE))
#> Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
#>   239

# Constant change and proportional change (Dual change model)
uni_lcsm_03 <- fit_uni_lcsm(data = data_uni_lcsm, 
                            var = c("x1", "x2", "x3", "x4", "x5"),
                            model = list(alpha_constant = TRUE, 
                                         beta = TRUE, 
                                         phi = FALSE))
#> Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
#>   239

Extract fit statistics

This function takes the lavaan objects as input and returns some fit statistics. More fit statistics can be returned using the argument details = TRUE.

# Extract fit statistics
fit_uni_lcsm <- extract_fit(uni_lcsm_01, uni_lcsm_02, uni_lcsm_03)

# Print table of parameter estimates
knitr::kable(fit_uni_lcsm, 
             digits = 3, 
             caption = "Parameter estimates for bivariate LCSM")
Parameter estimates for bivariate LCSM
model chisq npar aic bic cfi rmsea srmr
1 2761.423 3 7371.135 7383.772 0.000 0.569 2.070
2 17.734 6 4633.446 4658.721 0.998 0.023 0.068
3 11.615 7 4629.327 4658.815 1.000 0.000 0.054

Extract parameters

# Now extract parameter estimates
param_uni_lcsm_02 <- extract_param(uni_lcsm_03, printp = TRUE)

# Print table of parameter estimates
knitr::kable(param_uni_lcsm_02, 
             digits = 3, 
             caption = "Parameter estimates for bivariate LCSM")
Parameter estimates for bivariate LCSM
label estimate std.error statistic p.value std.lv std.all std.nox
gamma_lx1 21.066 0.060 348.365 < .001 17.456 17.456 17.456
sigma2_lx1 1.456 0.101 14.413 < .001 1.000 1.000 1.000
sigma2_ux 0.231 0.011 20.336 < .001 0.231 0.137 0.137
alpha_g2 -0.157 0.287 -0.547 .584 -0.450 -0.450 -0.450
sigma2_g2 0.122 0.015 7.916 < .001 1.000 1.000 1.000
sigma_g2lx1 0.264 0.033 7.961 < .001 0.627 0.627 0.627
beta_x -0.038 0.015 -2.606 .009 -0.142 -0.142 -0.142

TODOs

  • For now see example in README file on Github or shinyApp “shinychange”
  • step by step example here building all the way up to a univariate lcsm
  • select data
  • run univariate models
  • add parameters and compare results using anova and model parameters
  • use grimm 2017 data?