diy.sem.plot allows users to manually plot fully customisable path
diagrams for structural equation models (SEM) in R. It avoids the
inflexibility of automated SEM plotting tools and the tedium of drawing
diagrams in external applications.
Users manually specify node positions using x-y coordinates, and where
on the perimeter of each node (top, bottom, left, or right) a paths
should begin and end. From there, the primary function diyPaths()
renders the diagram, automatically inserting estimates centered on the
midpoint of paths and adjusting each node’s shape to match its variable
type. A range of fine-tuning options are included, facilitating the
creation of a path diagram exactly as envisioned, entirely within R.
Install the released version of diy.sem.plot from CRAN:
install.packages("diy.sem.plot")
Or install the development version from GitHub to get the latest
features, including the template() helper function (not yet on CRAN):
install.packages("remotes")
remotes::install_github("snagy86/diy.sem.plot", build_vignettes = TRUE)
The vignettes in the development version have not been updated yet.
Below is a brief example of using the package. For more examples, plus a full explanation of the workflow and available arguments, see the package’s vignette.
In this example, I used template() to generate the starting code,
saving time and avoiding syntax errors. It takes the fitted lavaan
model and prints the node list, path list and diyPaths() call with
default values. I then edited these values to position everything. The
original printed output is omitted here to save space.
The arguments in template() let you specify how much code is printed.
For example, diyPaths_arguments = "min" prints only the necessary
diyPaths() arguments, "max" prints all of them, and
include_variance_paths = FALSE leaves out the variance paths.
library(lavaan)
#> This is lavaan 0.7-2
#> lavaan is FREE software! Please report any bugs.
library(diy.sem.plot)
sem_model <- '
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
speed ~ visual + textual
visual ~~ textual
'
fit_sem <- sem(sem_model, data = HolzingerSwineford1939)
invisible(capture.output( # hiding default output
template(fit = fit_sem, include_variance_paths = TRUE, diyPaths_arguments = "max"))) # template() prints its code to the console with cat()
node_list <- list(
node(name = "visual", x = 1, y = 1, label = "Visual"),
node(name = "textual", x = 1, y = 3, label = "Textual"),
node(name = "speed", x = 4, y = 2, label = "Speed"),
node(name = "x1", x = 0, y = -0.5, label = "Visual\nPerception"),
node(name = "x2", x = 1, y = -0.5, label = "Cubes"),
node(name = "x3", x = 2, y = -0.5, label = "Lozenges"),
node(name = "x4", x = 0, y = 4.5, label = "Paragraph\nComprehension"),
node(name = "x5", x = 1, y = 4.5, label = "Sentence\nCompletion"),
node(name = "x6", x = 2, y = 4.5, label = "Word\nMeaning"),
node(name = "x7", x = 6, y = 1.25, label = "Speeded\nAddition"),
node(name = "x8", x = 6, y = 2, label = "Speeded\nCounting"),
node(name = "x9", x = 6, y = 2.75, label = "Speeded\nDiscrimination")
)
path_list <- list(
# regression paths
path(from = "visual", to = "speed", side_from = "right", side_to = "left", nudge_text_x = 0, nudge_text_y = 0),
path(from = "textual", to = "speed", side_from = "right", side_to = "left", nudge_text_x = 0, nudge_text_y = 0),
# loading paths
path(from = "visual", to = "x1", side_from = "bottom", side_to = "top", nudge_text_x = -0.1, nudge_text_y = 0),
path(from = "visual", to = "x2", side_from = "bottom", side_to = "top", nudge_text_x = 0, nudge_text_y = 0),
path(from = "visual", to = "x3", side_from = "bottom", side_to = "top", nudge_text_x = 0.1, nudge_text_y = 0),
path(from = "textual", to = "x4", side_from = "top", side_to = "bottom", nudge_text_x = -0.1, nudge_text_y = 0),
path(from = "textual", to = "x5", side_from = "top", side_to = "bottom", nudge_text_x = 0, nudge_text_y = 0),
path(from = "textual", to = "x6", side_from = "top", side_to = "bottom", nudge_text_x = 0.1, nudge_text_y = 0),
path(from = "speed", to = "x7", side_from = "right", side_to = "left", nudge_text_x = 0, nudge_text_y = -0.1),
path(from = "speed", to = "x8", side_from = "right", side_to = "left", nudge_text_x = 0, nudge_text_y = 0),
path(from = "speed", to = "x9", side_from = "right", side_to = "left", nudge_text_x = 0, nudge_text_y = 0.1),
# covariance/correlation paths
path(from = "textual", to = "visual", side_from = "left", side_to = "left", nudge_text_x = 0, nudge_text_y = 0, cov_curve = -0.6),
# variance paths
path(from = "x1", to = "x1", nudge_text_x = 0, nudge_text_y = 0, variance_position = "bottom"),
path(from = "x2", to = "x2", nudge_text_x = 0, nudge_text_y = 0, variance_position = "bottom"),
path(from = "x3", to = "x3", nudge_text_x = 0, nudge_text_y = 0, variance_position = "bottom"),
path(from = "x4", to = "x4", nudge_text_x = 0, nudge_text_y = 0, variance_position = "top"),
path(from = "x5", to = "x5", nudge_text_x = 0, nudge_text_y = 0, variance_position = "top"),
path(from = "x6", to = "x6", nudge_text_x = 0, nudge_text_y = 0, variance_position = "top"),
path(from = "x7", to = "x7", nudge_text_x = 0, nudge_text_y = 0, variance_position = "right"),
path(from = "x8", to = "x8", nudge_text_x = 0, nudge_text_y = 0, variance_position = "right"),
path(from = "x9", to = "x9", nudge_text_x = 0, nudge_text_y = 0, variance_position = "right"),
path(from = "visual", to = "visual", nudge_text_x = 0, nudge_text_y = 0, variance_position = "top"),
path(from = "textual", to = "textual", nudge_text_x = 0, nudge_text_y = 0, variance_position = "bottom"),
path(from = "speed", to = "speed", nudge_text_x = 0, nudge_text_y = 0, variance_position = "top")
)
my_sem_diagram <- diyPaths(fit = fit_sem, node_positions = node_list, path_positions = path_list,
standardised = TRUE,
digits = 3,
est_stars = TRUE,
est_p = FALSE,
est_ci = TRUE,
variance_stars = FALSE,
variance_p = FALSE,
variance_ci = FALSE,
sig_linetype = TRUE,
p_threshold = 0.05,
show_variances = TRUE,
show_grid = TRUE,
grid_axis_scale = 1,
non_transparent_text = TRUE,
latent_node_text_size = 6,
observed_node_text_size = 4.5,
path_text_size = 4.5,
line_thickness = 0.6,
arrow_size = 0.2,
node_width = 1.5, node_height = 1,
latent_node_size_adjust = 1,
observed_node_size_adjust = 0.5,
show_group_labels = FALSE,
panel_titles = NULL,
panel_cols = NULL,
margin_x = 0.3,
margin_y = 0.3,
look_up_table = TRUE)
print(my_sem_diagram)
#> $plot
#>
#> $look_up_table
#> type width height text_size
#> 1 latent 1.50 1.0 6.0
#> 2 observed 0.75 0.5 4.5
#> 3 path NA NA 4.5
A larger update and fully updated vignettes for the template()
workflow are on the way. After that, I plan to add support for Bayesian
SEM models fitted with blavaan.