manymome (version 0.2.3)

get_prod: Product Terms (if Any) Along a Path

Description

Identify the product term(s), if any, along a path in a model and return the term(s), with the variables involved and the coefficient(s) of the term(s).

Usage

get_prod(
  x,
  y,
  operator = ":",
  fit = NULL,
  est = NULL,
  data = NULL,
  expand = FALSE
)

Value

If at least one product term is found, it returns a list with these elements:

• prod: The names of the product terms found.

• b: The coefficients of these product terms.

• w: The variable, other than x, in each product term.

• x: The x-variable, that is, where the path starts.

• y: The y-variable, that is, where the path ends.

It returns NA if no product term is found along the path.

Arguments

x

Character. Variable name.

y

Character. Variable name.

operator

Character. The string used to indicate a product term. Default is ":", used in both lm() and lavaan::sem() for observed variables.

fit

The fit object. Currently only supports a lavaan::lavaan object. It can also be a lavaan.mi object returned by semTools::runMI() or its wrapper, such as semTools::sem.mi().

est

The output of lavaan::parameterEstimates(). If NULL, the default, it will be generated from fit. If supplied, fit will ge ignored.

data

Data frame (optional). If supplied, it will be used to identify the product terms.

expand

Whether products of more than two terms will be searched. FALSE by default.

Details

This function is used by several functions in manymome to identify product terms along a path. If possible, this is done by numerically checking whether a column in a dataset is the product of two other columns. If not possible (e.g., the "product term" is the "product" of two latent variables, formed by the products of indicators), then it requires the user to specify an operator.

The detailed workflow of this function can be found in the article https://sfcheung.github.io/manymome/articles/get_prod.html

This function is not intended to be used by users. It is exported such that advanced users or developers can use it.

Examples

dat <- modmed_x1m3w4y1
library(lavaan)
mod <-
"
m1 ~ x   + w1 + x:w1
m2 ~ m1  + w2 + m1:w2
m3 ~ m2
y  ~ m3  + w4 + m3:w4 + x + w3 + x:w3 + x:w4
"
fit <- sem(model = mod,
           data = dat,
           meanstructure = TRUE,
           fixed.x = FALSE)

# One product term
get_prod(x = "x", y = "m1", fit = fit)
# Two product terms
get_prod(x = "x", y = "y", fit = fit)
# No product term
get_prod(x = "m2", y = "m3", fit = fit)