Parameter estimates and fit statistics of LCSMs • lcsm

library(lcsm)
#> This is lcsm 0.3.2
#> Please report any issues or ideas at:
#> https://github.com/milanwiedemann/lcsm/issues
#> 
library(lavaan)
#> This is lavaan 0.6-14
#> 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.

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?