library(lcsm)
library(lavaan)
#> This is lavaan 0.6-13
#> 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:
extract_param(): only one row per estimated
parameter,extract_fit(): fit statistics for multiple
lavaan objects can be extracted.# 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:
#> 239This 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")| 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 |
# 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")| 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 |