semhelpinghands (version 0.1.10)

add_exo_cov: 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)

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".

Arguments

model

The model syntax to which the covariances are to be added.

FUN

Name (as string) of the lavaan wrapper to be called. Normally should be "sem", the default.

print

Logical. Whether the generated syntax should also be printed by cat(). Default is TRUE.

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

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

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.

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