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
?
- Reusable code. Don’t repeat yourself anymore when you only want to change a few things when comparing and fitting models.
-
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. - 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).
- 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.
- 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.
- 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.
- 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.
Regression example
# Define our regression terms
(regression <- list(mpg = names(mtcars)[2:5],
disp = names(mtcars)[4:7]))
#> $mpg
#> [1] "cyl" "disp" "hp" "drat"
#>
#> $disp
#> [1] "hp" "drat" "wt" "qsec"
# Load library
library(lavaanExtra)
#> Suggested citation: Thériault, R. (2022). lavaanExtra: Convenience functions for lavaan (R package version 0.0.4) [Computer software]. https://lavaanExtra.remi-theriault.com/
# Write the model, and check it
mtcars.model <- write_lavaan(regression = regression)
cat(mtcars.model)
#> ##################################################
#> # [---------Regressions (Direct effects)---------]
#>
#> mpg ~ cyl + disp + hp + drat
#> disp ~ hp + drat + wt + qsec
# Fit the model with `lavaan`
library(lavaan)
fit.reg <- lavaan(mtcars.model, data = mtcars, auto.var = TRUE)
# Get regression parameters only
lavaan_reg(fit.reg)
#> Outcome Predictor B p
#> 1 mpg cyl -0.242 0.244
#> 2 mpg disp -0.287 0.123
#> 3 mpg hp -0.264 0.128
#> 4 mpg drat 0.192 0.159
#> 5 disp hp 0.175 0.177
#> 6 disp drat -0.177 0.034
#> 7 disp wt 0.614 0.000
#> 8 disp qsec -0.186 0.061
# We can get it prettier with the `rempsyc::nice_table` integration
lavaan_reg(fit.reg, nice_table = TRUE, highlight = TRUE)
CFA example
# Define latent variables
(latent <- list(visual = paste0("x", 1:3),
textual = paste0("x", 4:6),
speed = paste0("x", 7:9)))
#> $visual
#> [1] "x1" "x2" "x3"
#>
#> $textual
#> [1] "x4" "x5" "x6"
#>
#> $speed
#> [1] "x7" "x8" "x9"
# 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 fit indices
nice_fit(fit.cfa)
#> Model chi2 df chi2.df p CFI TLI RMSEA SRMR AIC BIC
#> 1 fit.cfa 85.306 24 3.554 0 0.931 0.896 0.092 0.065 7517.49 7595.339
# We can get it prettier with the `rempsyc::nice_table` integration
nice_fit(fit.cfa, nice_table = TRUE)
But let’s say you had a bad fit and wanted to remove the three items with the lowest loadings, you can do so without have to respecify the model, only what items you wish to remove:
# Fit the model fit and plot with `lavaanExtra::cfa_fit_plot`
# to get the factor loadings visually (as PDF)
fit.cfa2 <- cfa_fit_plot(cfa.model, HolzingerSwineford1939,
remove.items = paste0("x", c(2:3, 7)))
#> lavaan 0.6-12 ended normally after 29 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 14
#>
#> Number of observations 301
#>
#> Model Test User Model:
#> Standard Robust
#> Test Statistic 8.442 7.313
#> Degrees of freedom 7 7
#> P-value (Chi-square) 0.295 0.397
#> Scaling correction factor 1.154
#> Yuan-Bentler correction (Mplus variant)
#>
#> Model Test Baseline Model:
#>
#> Test statistic 674.095 599.025
#> Degrees of freedom 15 15
#> P-value 0.000 0.000
#> Scaling correction factor 1.125
#>
#> User Model versus Baseline Model:
#>
#> Comparative Fit Index (CFI) 0.998 0.999
#> Tucker-Lewis Index (TLI) 0.995 0.999
#>
#> Robust Comparative Fit Index (CFI) 0.999
#> Robust Tucker-Lewis Index (TLI) 0.999
#>
#> Loglikelihood and Information Criteria:
#>
#> Loglikelihood user model (H0) -2429.864 -2429.864
#> Scaling correction factor 1.137
#> for the MLR correction
#> Loglikelihood unrestricted model (H1) -2425.644 -2425.644
#> Scaling correction factor 1.143
#> for the MLR correction
#>
#> Akaike (AIC) 4887.729 4887.729
#> Bayesian (BIC) 4939.628 4939.628
#> Sample-size adjusted Bayesian (BIC) 4895.228 4895.228
#>
#> Root Mean Square Error of Approximation:
#>
#> RMSEA 0.026 0.012
#> 90 Percent confidence interval - lower 0.000 0.000
#> 90 Percent confidence interval - upper 0.079 0.069
#> P-value RMSEA <= 0.05 0.713 0.814
#>
#> Robust RMSEA 0.013
#> 90 Percent confidence interval - lower 0.000
#> 90 Percent confidence interval - upper 0.078
#>
#> Standardized Root Mean Square Residual:
#>
#> SRMR 0.016 0.016
#>
#> 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 1.165 1.000
#> textual =~
#> x4 1.000 0.990 0.852
#> x5 1.115 0.066 16.910 0.000 1.104 0.857
#> x6 0.923 0.061 15.181 0.000 0.914 0.835
#> speed =~
#> x8 1.000 0.515 0.510
#> x9 1.722 0.398 4.322 0.000 0.887 0.881
#>
#> Covariances:
#> Estimate Std.Err z-value P(>|z|) Std.lv Std.all
#> visual ~~
#> textual 0.462 0.087 5.292 0.000 0.400 0.400
#> speed 0.266 0.072 3.674 0.000 0.443 0.443
#> textual ~~
#> speed 0.149 0.055 2.726 0.006 0.291 0.291
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|) Std.lv Std.all
#> .x1 0.000 0.000 0.000
#> .x4 0.370 0.050 7.356 0.000 0.370 0.274
#> .x5 0.441 0.056 7.822 0.000 0.441 0.266
#> .x6 0.362 0.047 7.689 0.000 0.362 0.302
#> .x8 0.756 0.090 8.407 0.000 0.756 0.740
#> .x9 0.228 0.167 1.359 0.174 0.228 0.224
#> visual 1.358 0.120 11.367 0.000 1.000 1.000
#> textual 0.981 0.121 8.093 0.000 1.000 1.000
#> speed 0.266 0.082 3.248 0.001 1.000 1.000
#>
#> R-Square:
#> Estimate
#> x1 1.000
#> x4 0.726
#> x5 0.734
#> x6 0.698
#> x8 0.260
#> x9 0.776
Let’s compare the fit to see if it’s better now:
nice_fit(fit.cfa, fit.cfa2, nice_table = TRUE)
It is! If you like this table, you may also wish to save it to Word. Also easy:
# Save fit table as an object
fit_table <- nice_fit(fit.cfa, fit.cfa2, nice_table = TRUE)
# Save fit table to Word!
save_as_docx(fit_table, path = "fit_table.docx")
SEM example
Here is a structural equation model example. We start with a path analysis first.
Saturated model
One might decide to look at the saturated lavaan
model first.
# Calculate scale averages
data <- HolzingerSwineford1939
data$visual <- rowMeans(data[paste0("x", 1:3)])
data$textual <- rowMeans(data[paste0("x", 4:6)])
data$speed <- rowMeans(data[paste0("x", 7:9)])
# Check what we have
head(data)
#> id sex ageyr agemo school grade x1 x2 x3 x4 x5 x6
#> 1 1 1 13 1 Pasteur 7 3.333333 7.75 0.375 2.333333 5.75 1.2857143
#> 2 2 2 13 7 Pasteur 7 5.333333 5.25 2.125 1.666667 3.00 1.2857143
#> 3 3 2 13 1 Pasteur 7 4.500000 5.25 1.875 1.000000 1.75 0.4285714
#> 4 4 1 13 2 Pasteur 7 5.333333 7.75 3.000 2.666667 4.50 2.4285714
#> 5 5 2 12 2 Pasteur 7 4.833333 4.75 0.875 2.666667 4.00 2.5714286
#> 6 6 2 14 1 Pasteur 7 5.333333 5.00 2.250 1.000000 3.00 0.8571429
#> x7 x8 x9 visual textual speed
#> 1 3.391304 5.75 6.361111 3.819444 3.123016 5.167472
#> 2 3.782609 6.25 7.916667 4.236111 1.984127 5.983092
#> 3 3.260870 3.90 4.416667 3.875000 1.059524 3.859179
#> 4 3.000000 5.30 4.861111 5.361111 3.198413 4.387037
#> 5 3.695652 6.30 5.916667 3.486111 3.079365 5.304106
#> 6 4.347826 6.65 7.500000 4.194444 1.619048 6.165942
# Define our variables
(M <- "visual")
#> [1] "visual"
(IV <- c("ageyr", "grade"))
#> [1] "ageyr" "grade"
(DV <- c("speed", "textual"))
#> [1] "speed" "textual"
# Define our lavaan lists
(mediation <- list(speed = M,
textual = M,
visual = IV))
#> $speed
#> [1] "visual"
#>
#> $textual
#> [1] "visual"
#>
#> $visual
#> [1] "ageyr" "grade"
(regression <- list(speed = IV,
textual = IV))
#> $speed
#> [1] "ageyr" "grade"
#>
#> $textual
#> [1] "ageyr" "grade"
(covariance <- list(speed = "textual",
ageyr = "grade"))
#> $speed
#> [1] "textual"
#>
#> $ageyr
#> [1] "grade"
# Write the model, and check it
model.saturated <- write_lavaan(mediation, regression, covariance)
cat(model.saturated)
#> ##################################################
#> # [-----------Mediations (named paths)-----------]
#>
#> speed ~ visual
#> textual ~ visual
#> visual ~ ageyr + grade
#>
#> ##################################################
#> # [---------Regressions (Direct effects)---------]
#>
#> speed ~ ageyr + grade
#> textual ~ ageyr + grade
#>
#> ##################################################
#> # [------------------Covariances-----------------]
#>
#> speed ~~ textual
#> ageyr ~~ grade
This looks good so far, but we might also want to check our indirect effects (mediations). For this, we have to obtain the path names by setting label = TRUE
. This will allow us to define our indirect effects and feed them back to write_lavaan
.
# We can run the model again. However, we set `label = TRUE` to get the path names
model.saturated <- write_lavaan(mediation, regression, covariance, label = TRUE)
cat(model.saturated)
#> ##################################################
#> # [-----------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
Here, if we check the mediation section of the model, we see that it has been “augmented” with the path names. Those are visual_speed
, visual_textual
, ageyr_visual
, and grade_visual
. The logic for the determination of the path names is predictable: it is always the predictor variable, on the left, followed by the predicted variable, on the right. So if we were to test all possible indirect effects, we would define our indirect
object as such:
# Define indirect object
(indirect <- list(ageyr_visual_speed = c("ageyr_visual", "visual_speed"),
ageyr_visual_textual = c("ageyr_visual", "visual_textual"),
grade_visual_speed = c("grade_visual", "visual_speed"),
grade_visual_textual = c("grade_visual", "visual_textual")))
#> $ageyr_visual_speed
#> [1] "ageyr_visual" "visual_speed"
#>
#> $ageyr_visual_textual
#> [1] "ageyr_visual" "visual_textual"
#>
#> $grade_visual_speed
#> [1] "grade_visual" "visual_speed"
#>
#> $grade_visual_textual
#> [1] "grade_visual" "visual_textual"
# Write the model, and check it
model.saturated <- write_lavaan(mediation, regression, covariance,
indirect, label = TRUE)
cat(model.saturated)
#> ##################################################
#> # [-----------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
If preferred (e.g., when dealing with long variable names), one can choose to use letters for the predictor variables. Note however that this tends to be somewhat more confusing and ambiguous.
# Write the model, and check it
model.saturated <- write_lavaan(mediation, regression, covariance,
label = TRUE, use.letters = TRUE)
cat(model.saturated)
#> ##################################################
#> # [-----------Mediations (named paths)-----------]
#>
#> speed ~ a_speed*visual
#> textual ~ a_textual*visual
#> visual ~ a_visual*ageyr + b_visual*grade
#>
#> ##################################################
#> # [---------Regressions (Direct effects)---------]
#>
#> speed ~ ageyr + grade
#> textual ~ ageyr + grade
#>
#> ##################################################
#> # [------------------Covariances-----------------]
#>
#> speed ~~ textual
#> ageyr ~~ grade
In this case, the path names are a_speed
, a_textual
, a_visual
, and b_visual
. So we would define our indirect
object as such:
# Define indirect object
(indirect <- list(ageyr_visual_speed = c("a_visual", "a_speed"),
ageyr_visual_textual = c("a_visual", "a_textual"),
grade_visual_speed = c("b_visual", "a_speed"),
grade_visual_textual = c("b_visual", "a_textual")))
#> $ageyr_visual_speed
#> [1] "a_visual" "a_speed"
#>
#> $ageyr_visual_textual
#> [1] "a_visual" "a_textual"
#>
#> $grade_visual_speed
#> [1] "b_visual" "a_speed"
#>
#> $grade_visual_textual
#> [1] "b_visual" "a_textual"
# Write the model, and check it
model.saturated <- write_lavaan(mediation, regression, covariance,
indirect, label = TRUE, use.letters = TRUE)
cat(model.saturated)
#> ##################################################
#> # [-----------Mediations (named paths)-----------]
#>
#> speed ~ a_speed*visual
#> textual ~ a_textual*visual
#> visual ~ a_visual*ageyr + b_visual*grade
#>
#> ##################################################
#> # [---------Regressions (Direct effects)---------]
#>
#> speed ~ ageyr + grade
#> textual ~ ageyr + grade
#>
#> ##################################################
#> # [------------------Covariances-----------------]
#>
#> speed ~~ textual
#> ageyr ~~ grade
#>
#> ##################################################
#> # [--------Mediations (indirect effects)---------]
#>
#> ageyr_visual_speed := a_visual * a_speed
#> ageyr_visual_textual := a_visual * a_textual
#> grade_visual_speed := b_visual * a_speed
#> grade_visual_textual := b_visual * a_textual
There is also an experimental feature that attempts to produce the indirect effects automatically. This feature requires specifying your dependent and mediator variables as “DV” and “M”, respectively, in the indirect
object. In our case, we have already defined those earlier, so we can just feed the proper objects.
# Define indirect object
(indirect <- list(M = M,
DV = DV))
#> $M
#> [1] "visual"
#>
#> $DV
#> [1] "speed" "textual"
# Write the model, and check it
model.saturated <- write_lavaan(mediation, regression, covariance,
indirect, label = TRUE)
cat(model.saturated)
#> ##################################################
#> # [-----------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
We are now satisfied with our model, so we can finally fit it!
# Fit the model with `lavaan`
fit.saturated <- lavaan(model.saturated, data = data, auto.var = TRUE)
# Get regression parameters only and make it pretty with the `rempsyc::nice_table` integration
lavaan_reg(fit.saturated, nice_table = TRUE, highlight = TRUE)
So speed
as predicted by ageyr
isn’t significant. We could remove that path from the model it if we are trying to make a more parsimonious model. Let’s make the non-saturated path analysis model next.
Path analysis model
Because we use lavaanExtra
, we don’t have to redefine the entire model: simply what we want to update. In this case, the regressions and the indirect effects.
(regression <- list(speed = "grade",
textual = IV))
#> $speed
#> [1] "grade"
#>
#> $textual
#> [1] "ageyr" "grade"
# We check that we have removed "ageyr" correctly from "speed". OK.
# We can run the model again, setting `label = TRUE` to get the path names
model.path <- write_lavaan(mediation, regression, covariance, label = TRUE)
cat(model.path)
#> ##################################################
#> # [-----------Mediations (named paths)-----------]
#>
#> speed ~ visual_speed*visual
#> textual ~ visual_textual*visual
#> visual ~ ageyr_visual*ageyr + grade_visual*grade
#>
#> ##################################################
#> # [---------Regressions (Direct effects)---------]
#>
#> speed ~ grade
#> textual ~ ageyr + grade
#>
#> ##################################################
#> # [------------------Covariances-----------------]
#>
#> speed ~~ textual
#> ageyr ~~ grade
# We check that we have removed "ageyr" correctly from "speed" in the
# regression section. OK.
# Define just our indirect effects of interest
(indirect <- list(age_visual_speed = c("ageyr_visual", "visual_speed"),
grade_visual_textual = c("grade_visual", "visual_textual")))
#> $age_visual_speed
#> [1] "ageyr_visual" "visual_speed"
#>
#> $grade_visual_textual
#> [1] "grade_visual" "visual_textual"
# We run the model again, with the indirect effects
model.path <- write_lavaan(mediation, regression, covariance,
indirect, label = TRUE)
cat(model.path)
#> ##################################################
#> # [-----------Mediations (named paths)-----------]
#>
#> speed ~ visual_speed*visual
#> textual ~ visual_textual*visual
#> visual ~ ageyr_visual*ageyr + grade_visual*grade
#>
#> ##################################################
#> # [---------Regressions (Direct effects)---------]
#>
#> speed ~ grade
#> textual ~ ageyr + grade
#>
#> ##################################################
#> # [------------------Covariances-----------------]
#>
#> speed ~~ textual
#> ageyr ~~ grade
#>
#> ##################################################
#> # [--------Mediations (indirect effects)---------]
#>
#> age_visual_speed := ageyr_visual * visual_speed
#> grade_visual_textual := grade_visual * visual_textual
# Fit the model with `lavaan`
fit.path <- lavaan(model.path, data = data, auto.var = TRUE)
# Get regression parameters only and make it pretty with the `rempsyc::nice_table` integration
lavaan_reg(fit.path, nice_table = TRUE, highlight = TRUE)
# We only kept significant regressions. Good (for this demo).
# Get covariance indices
lavaan_cov(fit.path)
#> Variable.1 Variable.2 r p
#> 8 speed textual 0.131 0.024
#> 9 ageyr grade 0.511 0.000
#> 10 speed speed 0.824 0.000
#> 11 textual textual 0.765 0.000
#> 12 visual visual 0.942 0.000
#> 13 ageyr ageyr 1.000 0.000
#> 14 grade grade 1.000 0.000
# We can get it prettier with the `rempsyc::nice_table` integration
lavaan_cov(fit.path, nice_table = TRUE)
# Get fit indices
nice_fit(fit.cfa, fit.saturated, fit.path)
#> Model chi2 df chi2.df p CFI TLI RMSEA SRMR AIC
#> 1 fit.cfa 85.306 24 3.554 0.000 0.931 0.896 0.092 0.065 7517.490
#> 2 fit.saturated 0.000 0 NaN NA 1.000 1.000 0.000 0.000 3483.460
#> 3 fit.path 0.327 1 0.327 0.568 1.000 1.028 0.000 0.007 3481.787
#> BIC
#> 1 7595.339
#> 2 3539.017
#> 3 3533.640
# We can get it prettier with the `rempsyc::nice_table` integration
nice_fit(fit.cfa, fit.saturated, fit.path, nice_table = TRUE)
# Let's get the indirect effects only
lavaan_ind(fit.path)
#> Indirect.Effect Paths B p
#> 15 age_visual_speed ageyr_visual*visual_speed -0.033 0.037
#> 16 grade_visual_textual grade_visual*visual_textual 0.066 0.002
# We can get it prettier with the `rempsyc::nice_table` integration
lavaan_ind(fit.path, nice_table = TRUE)
# Get modification indices only
modindices(fit.path, sort = TRUE, maximum.number = 5)
#> lhs op rhs mi epc sepc.lv sepc.all sepc.nox
#> 29 visual ~ textual 0.326 1.622 1.622 1.975 1.975
#> 35 grade ~ textual 0.326 -0.228 -0.228 -0.488 -0.488
#> 34 grade ~ speed 0.326 -0.038 -0.038 -0.062 -0.062
#> 19 speed ~~ grade 0.326 -0.021 -0.021 -0.056 -0.056
#> 25 speed ~ textual 0.326 -0.067 -0.067 -0.087 -0.087
For reference, this is our model, visually speaking
Latent model
Finally, perhaps we change our mind and decide to run a full SEM instead, with latent variables. Fear not: we don’t have to redo everything again. We can simply define our latent variables and proceed. In this example, we have already defined our latent variable for our CFA earlier, so we don’t even need to write that again!
model.latent <- write_lavaan(mediation, regression, covariance,
indirect, latent, label = TRUE)
cat(model.latent)
#> ##################################################
#> # [---------------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 ~ grade
#> textual ~ ageyr + grade
#>
#> ##################################################
#> # [------------------Covariances-----------------]
#>
#> speed ~~ textual
#> ageyr ~~ grade
#>
#> ##################################################
#> # [--------Mediations (indirect effects)---------]
#>
#> age_visual_speed := ageyr_visual * visual_speed
#> grade_visual_textual := grade_visual * visual_textual
# Run model
fit.latent <- lavaan(model.latent, data = HolzingerSwineford1939, auto.var = TRUE,
auto.fix.first = TRUE, auto.cov.lv.x = TRUE)
# Get fit indices
nice_fit(fit.cfa, fit.saturated, fit.path, fit.latent)
#> Model chi2 df chi2.df p CFI TLI RMSEA SRMR AIC
#> 1 fit.cfa 85.306 24 3.554 0.000 0.931 0.896 0.092 0.065 7517.490
#> 2 fit.saturated 0.000 0 NaN NA 1.000 1.000 0.000 0.000 3483.460
#> 3 fit.path 0.327 1 0.327 0.568 1.000 1.028 0.000 0.007 3481.787
#> 4 fit.latent 118.917 37 3.214 0.000 0.924 0.888 0.086 0.061 8638.789
#> BIC
#> 1 7595.339
#> 2 3539.017
#> 3 3533.640
#> 4 8746.198
# We can get it prettier with the `rempsyc::nice_table` integration
nice_fit(fit.cfa, fit.saturated, fit.path, fit.latent, nice_table = TRUE)
Special cases
Finally, intercepts and various constraints can also be specified, e.g.:
intercept <- c("mpg", "cyl", "disp")
constraint.equal <- list(b1 = "(b2 + b3)^2")
constraint.smaller <- list(b1 = "exp(b2 + b3)")
constraint.larger <- list(b1 = "exp(b2 + b3)")
custom <- "y1 + y2 ~ f1 + f2 + x1 + x2"
model.custom <- write_lavaan(
intercept = intercept, constraint.equal = constraint.equal,
constraint.smaller = constraint.smaller,
constraint.larger = constraint.larger, custom = custom)
cat(model.custom)
#> ##################################################
#> # [------------------Intercepts------------------]
#>
#> mpg ~ 1
#> cyl ~ 1
#> disp ~ 1
#>
#> ##################################################
#> # [-----------------Constraints------------------]
#>
#> b1 == (b2 + b3)^2
#> b1 < exp(b2 + b3)
#> b1 > exp(b2 + b3)
#>
#> ##################################################
#> # [------------Custom Specifications-------------]
#>
#> y1 + y2 ~ f1 + f2 + x1 + x2
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.
Support me and this package
Thank you for your support. You can support me and this package here: https://github.com/sponsors/rempsyc