| add_exo_cov {semhelpinghands} | R Documentation |
Add Covariances Between Exogenous Variables
Description
It generates the
'lavaan' model syntax for exogenous
variables in a lavaan model.
Usage
add_exo_cov(model, FUN = "sem", print = TRUE)
auto_exo_cov(model, FUN = "sem", print = TRUE)
Arguments
model |
The model syntax to which the covariances are to be added. |
FUN |
Name (as string) of the
|
print |
Logical. Whether the
generated syntax should also be
printed by |
Details
The function lavaan::sem()
usually will set covariances between
"exogenous" variables free when
fixed.x = FALSE ("exogenous" is
defined here as variables that appear
on the right hand side but not on the
left hand side of the ~ operator').
However, if a covariance between the
residual term of an endogenous
variable and an exogenous variable is
manually set to free, lavaan::sem()
may not set the aforementioned
covariances free. Users will need to
free them manually, and there may be
a lot of them in some models.
This function gets a model syntax and generates the syntax for these covariances. Users can then inspect it, modify it if necessary, and then copy and paste it to the model syntax.
Value
add_exo_cov() returns a one-element
character vector of the syntax, with
lines separated by "\n". The
generated syntax is appended to the
input model syntax.
auto_exo_cov() returns a
one-element character vector of the
generated syntax, with lines
separated by "\n".
Functions
-
add_exo_cov(): Add covariances between exogenous variables to the model syntax and than return the modified model syntax. -
auto_exo_cov(): Generate the model syntax for the covariances between exogenous variables.
Author(s)
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
Examples
library(lavaan)
set.seed(8976223)
n <- 100
x <- rnorm(n)
m <- .5 * x + rnorm(n, 0, sqrt(.4))
z <- rnorm(n)
y <- .4 * m + .3 * z * m + rnorm(n, 0, .5)
dat <- data.frame(x, m, z, y)
dat$zm <- dat$z * dat$m
mod0 <-
"
m ~ x
y ~ m + z + zm
m ~~ z + zm
"
fit <- sem(mod0, dat, fixed.x = FALSE)
# Add covariances. Also printed by default.
mod0_cov <- add_exo_cov(mod0)
# Fit the model
fit_cov <- sem(mod0_cov, dat, fixed.x = FALSE)
# Manually adding the covariances
mod1 <-
"
m ~ x
y ~ m + z + zm
m ~~ z + zm
z ~~ zm + x
zm ~~ x
"
fit1 <- sem(mod1, dat, meanstructure = TRUE, fixed.x = FALSE)
# Compare the results
# No manual covariances
fit
# Automatically generated covariances
fit_cov
# Manually added covariances
fit1