Create path diagrams to visualise model specifications

Plot simplified path diagrams of LCS models

This function is work in progress and can only plot univariate and bivariate LCS models that were specified with fit_uni_lcsm() or fit_bi_lcsm(). Modified LCS models will probably return errors as the layout matrix that gets created by this plot function only supports the specifications that can be modelled with this package. The input arguments for plotting a simplified path dioagram are:

the estimated lavaan object lavaan_object,
the lavaan_syntax and ,
lcsm indicating whether the LCS model is “univariate” or “bivariate”

Optional arguments can be used to change the look of the plot, for example:

lcsm_colours can be used to highlight the different parts of the latent change score model
- white: observed scores
- green: latent true scores
- blue: latent change scores
- yellow: latent change scores

Further arguments can be passed on to semPlot::semPaths(), for example:

what, “path” to show unweighted gray edges, “par” to show parameter estimates as weighted (green/red) edges
whatLabels, “label” to show edege names as label or “est” for parameter estimates, “hide” to hide edge labels

Univariate LCS model

# Fit bivariate lcsm and save the results 
uni_lavaan_results <- fit_uni_lcsm(data = data_uni_lcsm, 
                                   var = c("x1", "x2", "x3", "x4", "x5"),
                                   model = list(alpha_constant = TRUE, 
                                                beta = FALSE, 
                                                phi = TRUE)
                                  )
#> Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
#>   239

# Save the lavaan syntax that is used to create the layout matrix for semPlot
uni_lavaan_syntax <- fit_uni_lcsm(data = data_uni_lcsm, 
                                  var = c("x1", "x2", "x3", "x4", "x5"),
                                  model = list(alpha_constant = TRUE, 
                                               beta = FALSE, 
                                               phi = TRUE),
                                  return_lavaan_syntax = TRUE)

# Plot the results
plot_lcsm(lavaan_object = uni_lavaan_results,
          lavaan_syntax = uni_lavaan_syntax,
          edge.label.cex = .9,  
          lcsm_colours = TRUE,
          lcsm = "univariate")
#> Registered S3 methods overwritten by 'huge':
#>   method    from   
#>   plot.sim  BDgraph
#>   print.sim BDgraph

Bivariate LCS model

# Fit bivariate lcsm and save the results 
bi_lavaan_results <- fit_bi_lcsm(data = data_bi_lcsm, 
                                 var_x = c("x1", "x2", "x3", "x4", "x5"),
                                 var_y = c("y1", "y2", "y3", "y4", "y5"),
                                 model_x = list(alpha_constant = TRUE, 
                                                beta = TRUE, 
                                                phi = FALSE),
                                 model_y = list(alpha_constant = TRUE, 
                                                beta = TRUE, 
                                                phi = TRUE),
                                 coupling = list(delta_lag_xy = TRUE, 
                                                 xi_lag_yx = TRUE))

# Save the lavaan syntax that is used to create the layout matrix for semPlot
bi_lavaan_syntax <- fit_bi_lcsm(data = data_bi_lcsm, 
                                var_x = c("x1", "x2", "x3", "x4", "x5"),
                                var_y = c("y1", "y2", "y3", "y4", "y5"),
                                model_x = list(alpha_constant = TRUE, 
                                               beta = TRUE, 
                                               phi = FALSE),
                                model_y = list(alpha_constant = TRUE, 
                                               beta = TRUE, 
                                               phi = TRUE),
                                coupling = list(delta_lag_xy = TRUE, 
                                                xi_lag_yx = TRUE),
                                return_lavaan_syntax = TRUE)

# Plot the results
plot_lcsm(lavaan_object = bi_lavaan_results, 
          lavaan_syntax = bi_lavaan_syntax,
          lcsm_colours = TRUE,
          whatLabels = "hide",
          lcsm = "bivariate")