Quick Start To set_cfa_layout
Shu Fai Cheung & Mark Hok Chio Lai
20231112
Source:vignettes/quick_start_cfa.Rmd
quick_start_cfa.Rmd
Introduction
The package semptools (CRAN page)
contains functions that postprocess an output from
semPlot::semPaths()
, to help users to customize the
appearance of the graphs generated by semPlot::semPaths()
.
For the introduction to functions for doing very specific tasks, such as
moving the parameter estimate of a path or rotating the residual of a
variable, please refer to vignette("semptools")
. The
present guide focuses on how to use set_cfa_layout()
to
configure various aspects of an semPaths
graph generated
for a typical confirmatory factor analysis (CFA) model.
The Initial semPaths Graph
Let us consider a CFA model. We will use cfa_example
, a
sample CFA dataset from semptools
with 14 variables for
illustration.
library(semptools)
head(round(cfa_example, 3), 3)
#> x01 x02 x03 x04 x05 x06 x07 x08 x09 x10 x11
#> 1 1.159 1.271 1.451 0.691 0.015 0.212 0.336 1.559 0.870 1.115 1.251
#> 2 0.059 0.496 0.585 1.800 0.555 0.012 1.208 0.551 0.055 0.365 0.142
#> 3 0.737 2.933 1.625 0.642 1.218 0.155 0.861 0.862 0.738 2.443 0.628
#> x12 x13 x14
#> 1 0.253 0.663 1.049
#> 2 0.110 0.207 0.226
#> 3 1.604 1.688 0.395
This is the CFA model to be fitted:
mod <
'f1 =~ x01 + x02 + x03
f2 =~ x04 + x05 + x06 + x07
f3 =~ x08 + x09 + x10
f4 =~ x11 + x12 + x13 + x14
'
Fitting the model by lavaan::cfa()
library(lavaan)
#> This is lavaan 0.616
#> lavaan is FREE software! Please report any bugs.
fit < lavaan::cfa(mod, cfa_example)
This is the plot from semPlot::semPaths()
:
library(semPlot)
p < semPaths(fit, whatLabels="est",
sizeMan = 3.25,
node.width = 1,
edge.label.cex = .75,
style = "ram",
mar = c(10, 5, 10, 5))
The default layout is sufficient to have a quick examination of the
results. We will see how set_cfa_layout()
can be used to do
the following tasks to postprocess the graph:
Change the order of the indicators.
Change the order of the factors.
Change the curvature of the interfactor covariances.
Move the loadings along the paths from factors to indicators.
Rotate the graph.
Order the Indicators and Factors
Suppose we want to do this:

Order the factors this way, from the left to the right:
f2, f1, f4, f3

Order the indicators this way, from the left to the right:
x04, x05, x06, x07, x01, x02, x03, x11, x12, x13, x14, x08, x09, x10

We would like to place the factors this way:
f2
above the center ofx04
,x05
,x06
, andx07
.f1
above the center ofx01
,x02
, andx03
.f4
above the center ofx11
,x12
,x13
, andx14
.f3
above the center ofx08
,x09
, andx10
.
To do this, we create two vectors, one for the argument
indicator_order
and the other for the argument
indicator_factor
.

indicator_order
is a string vector with length equal to the number of indicators, with the desired order. In this example, it will be like this:
indicator_order < c("x04", "x05", "x06", "x07",
"x01", "x02", "x03",
"x11", "x12", "x13", "x14",
"x08", "x09", "x10")

indicator_factor
is a string vector with length equal to the number of indicators. The elements are the names of the latent factors, denoting which indicators will be used to compute the mean positions to place the latent factors:
indicator_factor < c( "f2", "f2", "f2", "f2",
"f1", "f1", "f1",
"f4", "f4", "f4", "f4",
"f3", "f3", "f3")
The set_cfa_layout()
function needs at least three
arguments:
semPaths_plot
: ThesemPaths
plot.indicator_order
: The vector for the order of indicators.indicator_factor
: The vector for the placement of the latent factors.
They do not have to be named if they are in this order.
We now use set_cfa_layout()
to postprocess the
graph:
p2 < set_cfa_layout(p,
indicator_order,
indicator_factor)
plot(p2)
Change the Curvatures of the Factor Covariances
The graph has the factors and indicators ordered as required.
However, the interfactor covariances are too close to the factors. To
increases the curvatures of the covariances, we can use the argument
fcov_curve
. The default is .4. Let us increase it to
1.75.
p2 < set_cfa_layout(p,
indicator_order,
indicator_factor,
fcov_curve = 1.75)
plot(p2)
The covariances are now more readable. The exact effect of the values vary from graph to graph. Therefore, trial and error is required to find a value suitable for a graph.
Move the Loadings
We can also move all the factor loadings together using the argument
loading_position
. The default value is .5, at the middle of
the paths. If we want to move the loadings closer to the indicators, we
increase this number. If we want to move the loadings closer to the
indicators, we decrease this number. In the following example, we move
the loadings closer to the indicators, and increase the distance between
them in the process.
p2 < set_cfa_layout(p,
indicator_order,
indicator_factor,
fcov_curve = 1.75,
loading_position = .8)
plot(p2)
The factor loadings are now easier to read, and also closer to the corresponding indicators.
Rotate the Model
The default orientation is “pointing downwards”: latent factors on
the top, pointing down to the indicators on the bottom. The orientation
can be set to one of these four directions: down (default), left, up,
and right. This is done by the argument point_to
.
p2 < set_cfa_layout(p,
indicator_order,
indicator_factor,
fcov_curve = 1.75,
loading_position = .8,
point_to = "up")
plot(p2)
Pipe
Like other functions in semptools
, the
set_cfa_layout()
function can be chained with other
functions using the pipe operator, %>%
, from the package
magrittr
, or the native pipe operator >
available since R 4.1.x. Suppose we want to mark the significant test
results for the free parameters using mark_sig()
:
# If R version >= 4.1.0
p2 < set_cfa_layout(p,
indicator_order,
indicator_factor,
fcov_curve = 1.75,
loading_position = .9,
point_to = "up") >
mark_sig(fit)
plot(p2)
#> Loading required package: magrittr