lavaanExtra: Convenience functions for lavaan

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Affords an alternative, vector-based syntax to lavaan, as well as other convenience functions such as naming paths and defining indirect links automatically. Also offers convenience formatting optimized for a publication and script sharing workflow.

Installation

You can install the development version of lavaanExtra like so:

install.packages("lavaanExtra", repos = c(
  rempsyc = "https://rempsyc.r-universe.dev",
  CRAN = "https://cloud.r-project.org"))

Why use lavaanExtra?

  1. Reusable code. Don’t repeat yourself anymore when you only want to change a few things when comparing and fitting models.
  2. Shorter code. Because of point 1, you can have shorter code, since you write it once and simply reuse it. For items with similar patterns, you can also use paste0() with appropriate item numbers instead of typing each one every time.
  3. Less error-prone code. Because of point 1, you can have less risk of human errors since you don’t have possibly multiple different version of the same thing (which makes it easier to correct too).
  4. Better control over your code. Because of point 1, you are in control of the whole flow. You change it once, and it will change it everywhere else in the script, without having to change it manually for each model.
  5. More readable code. Because of point 1, other people (but also yourself) only have to process the information the first time to make sure it’s been specified correctly, and not every time you check the new models.
  6. Prettier code. Because it will format the model for you in a pretty way, every time. You don’t have to worry about manually making your model good-looking and readable anymore.
  7. More accessible code. You don’t have to remember the exact syntax (although it is recommended) for it to work. It uses intuitive variable names that most people can understand. This benefit is most apparent for beginners, but it also saves precious typing time for veterans.

Overview

CFA example

SEM example

Final note

CFA example

# Load library
library(lavaan)
#> This is lavaan 0.6-12
#> lavaan is FREE software! Please report any bugs.
library(lavaanExtra)
#> Suggested citation: Thériault, R. (2022).lavaanExtra: Convenience functions for lavaan 
#> (R package version 0.1.1) [Computer software]. https://lavaanExtra.remi-theriault.com/

# Define latent variables
latent <- list(visual = paste0("x", 1:3),
               textual = paste0("x", 4:6),
               speed = paste0("x", 7:9))

# Write the model, and check it
cfa.model <- write_lavaan(latent = latent)
cat(cfa.model)
#> ##################################################
#> # [---------------Latent variables---------------]
#> 
#> visual =~ x1 + x2 + x3
#> textual =~ x4 + x5 + x6
#> speed =~ x7 + x8 + x9

# Fit the model fit and plot with `lavaanExtra::cfa_fit_plot`
# to get the factor loadings visually (optionally as PDF)
fit.cfa <- cfa_fit_plot(cfa.model, HolzingerSwineford1939)
#> lavaan 0.6-12 ended normally after 35 iterations
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                        21
#> 
#>   Number of observations                           301
#> 
#> Model Test User Model:
#>                                               Standard      Robust
#>   Test Statistic                                85.306      87.132
#>   Degrees of freedom                                24          24
#>   P-value (Chi-square)                           0.000       0.000
#>   Scaling correction factor                                  0.979
#>     Yuan-Bentler correction (Mplus variant)                       
#> 
#> Model Test Baseline Model:
#> 
#>   Test statistic                               918.852     880.082
#>   Degrees of freedom                                36          36
#>   P-value                                        0.000       0.000
#>   Scaling correction factor                                  1.044
#> 
#> User Model versus Baseline Model:
#> 
#>   Comparative Fit Index (CFI)                    0.931       0.925
#>   Tucker-Lewis Index (TLI)                       0.896       0.888
#>                                                                   
#>   Robust Comparative Fit Index (CFI)                         0.930
#>   Robust Tucker-Lewis Index (TLI)                            0.895
#> 
#> Loglikelihood and Information Criteria:
#> 
#>   Loglikelihood user model (H0)              -3737.745   -3737.745
#>   Scaling correction factor                                  1.133
#>       for the MLR correction                                      
#>   Loglikelihood unrestricted model (H1)      -3695.092   -3695.092
#>   Scaling correction factor                                  1.051
#>       for the MLR correction                                      
#>                                                                   
#>   Akaike (AIC)                                7517.490    7517.490
#>   Bayesian (BIC)                              7595.339    7595.339
#>   Sample-size adjusted Bayesian (BIC)         7528.739    7528.739
#> 
#> Root Mean Square Error of Approximation:
#> 
#>   RMSEA                                          0.092       0.093
#>   90 Percent confidence interval - lower         0.071       0.073
#>   90 Percent confidence interval - upper         0.114       0.115
#>   P-value RMSEA <= 0.05                          0.001       0.001
#>                                                                   
#>   Robust RMSEA                                               0.092
#>   90 Percent confidence interval - lower                     0.072
#>   90 Percent confidence interval - upper                     0.114
#> 
#> Standardized Root Mean Square Residual:
#> 
#>   SRMR                                           0.065       0.065
#> 
#> Parameter Estimates:
#> 
#>   Standard errors                             Sandwich
#>   Information bread                           Observed
#>   Observed information based on                Hessian
#> 
#> Latent Variables:
#>                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
#>   visual =~                                                             
#>     x1                1.000                               0.900    0.772
#>     x2                0.554    0.132    4.191    0.000    0.498    0.424
#>     x3                0.729    0.141    5.170    0.000    0.656    0.581
#>   textual =~                                                            
#>     x4                1.000                               0.990    0.852
#>     x5                1.113    0.066   16.946    0.000    1.102    0.855
#>     x6                0.926    0.061   15.089    0.000    0.917    0.838
#>   speed =~                                                              
#>     x7                1.000                               0.619    0.570
#>     x8                1.180    0.130    9.046    0.000    0.731    0.723
#>     x9                1.082    0.266    4.060    0.000    0.670    0.665
#> 
#> Covariances:
#>                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
#>   visual ~~                                                             
#>     textual           0.408    0.099    4.110    0.000    0.459    0.459
#>     speed             0.262    0.060    4.366    0.000    0.471    0.471
#>   textual ~~                                                            
#>     speed             0.173    0.056    3.081    0.002    0.283    0.283
#> 
#> Variances:
#>                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
#>    .x1                0.549    0.156    3.509    0.000    0.549    0.404
#>    .x2                1.134    0.112   10.135    0.000    1.134    0.821
#>    .x3                0.844    0.100    8.419    0.000    0.844    0.662
#>    .x4                0.371    0.050    7.382    0.000    0.371    0.275
#>    .x5                0.446    0.057    7.870    0.000    0.446    0.269
#>    .x6                0.356    0.047    7.658    0.000    0.356    0.298
#>    .x7                0.799    0.097    8.222    0.000    0.799    0.676
#>    .x8                0.488    0.120    4.080    0.000    0.488    0.477
#>    .x9                0.566    0.119    4.768    0.000    0.566    0.558
#>     visual            0.809    0.180    4.486    0.000    1.000    1.000
#>     textual           0.979    0.121    8.075    0.000    1.000    1.000
#>     speed             0.384    0.107    3.596    0.000    1.000    1.000
#> 
#> R-Square:
#>                    Estimate
#>     x1                0.596
#>     x2                0.179
#>     x3                0.338
#>     x4                0.725
#>     x5                0.731
#>     x6                0.702
#>     x7                0.324
#>     x8                0.523
#>     x9                0.442

# Get nice fit indices with the `rempsyc::nice_table` integration
nice_fit(fit.cfa, nice_table = TRUE)

SEM example

Note that that latent variables have been defined above, so we can reuse them as is, without having to redefine them.

# Define our other variables
M <- "visual"
IV <- c("ageyr", "grade")
DV <- c("speed", "textual")

# Define our lavaan lists
mediation <- list(speed = M, textual = M, visual = IV)
regression <- list(speed = IV, textual = IV)
covariance <- list(speed = "textual", ageyr = "grade")

# Define indirect effects object
indirect <- list(IV = IV, M = M, DV = DV)

# Write the model, and check it
model <- write_lavaan(mediation, regression, covariance, 
                      indirect, latent, label = TRUE)
cat(model)
#> ##################################################
#> # [---------------Latent variables---------------]
#> 
#> visual =~ x1 + x2 + x3
#> textual =~ x4 + x5 + x6
#> speed =~ x7 + x8 + x9
#> 
#> ##################################################
#> # [-----------Mediations (named paths)-----------]
#> 
#> speed ~ visual_speed*visual
#> textual ~ visual_textual*visual
#> visual ~ ageyr_visual*ageyr + grade_visual*grade
#> 
#> ##################################################
#> # [---------Regressions (Direct effects)---------]
#> 
#> speed ~ ageyr + grade
#> textual ~ ageyr + grade
#> 
#> ##################################################
#> # [------------------Covariances-----------------]
#> 
#> speed ~~ textual
#> ageyr ~~ grade
#> 
#> ##################################################
#> # [--------Mediations (indirect effects)---------]
#> 
#> ageyr_visual_speed := ageyr_visual * visual_speed
#> ageyr_visual_textual := ageyr_visual * visual_textual
#> grade_visual_speed := grade_visual * visual_speed
#> grade_visual_textual := grade_visual * visual_textual

fit.sem <- sem(model, data = HolzingerSwineford1939)

# Get regression parameters only and make it pretty with the `rempsyc::nice_table` integration
lavaan_reg(fit.sem, nice_table = TRUE, highlight = TRUE)

# Get covariance indices and make it pretty with the `rempsyc::nice_table` integration
lavaan_cov(fit.sem, nice_table = TRUE)

# Get nice fit indices with the `rempsyc::nice_table` integration
fit_table <- nice_fit(fit.cfa, fit.sem, nice_table = TRUE)
fit_table

# Save fit table to Word!
save_as_docx(fit_table, path = "fit_table.docx")

# Let's get the indirect effects only and make it pretty with the `rempsyc::nice_table` integration
lavaan_ind(fit.sem, nice_table = TRUE)

# Plot our model
nice_lavaanPlot(fit.sem)

Final note

This is an experimental package in a very early stage. Any feedback or feature request is appreciated, and the package will likely change and evolve over time based on community feedback. Feel free to open an issue or discussion to share your questions or concerns. And of course, please have a look at the other tutorials to discover even more cool features: https://lavaanextra.remi-theriault.com/articles/

Support me and this package

Thank you for your support. You can support me and this package here: https://github.com/sponsors/rempsyc