interactions with ordered data

In modsem: Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)

EVAL_DEFAULT <- FALSE
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = EVAL_DEFAULT
)

library(modsem)
set.seed(2938472)

modsem implements a Monte-Carlo correction for LMS and QML models with ordinal data. Here we refer to these informally as MC-LMS-ORD and MC-QML-ORD.

The MC-LMS-ORD and MC-QML-ORD algorithms are based on Slupphaug, Mehmetoglu, and Mittner (2026) For a more direct implementation of the original algorithm, we recommend checking out the plssem package.

Example

Here we ordinalize the data in the oneInt dataset.

ordinalize <- function(x, probs = c(0, 0.35, 0.7, 1)) {
  x <- (x - mean(x)) / sd(x)
  cut(
    x,
    breaks = stats::quantile(x, probs = probs),
    include.lowest = TRUE,
    ordered_result = TRUE
  )
}

oneIntOrd <- as.data.frame(lapply(oneInt, ordinalize))

Now we can estimate our model, indicating which variables are ordinal, using the ordered= argument.

model <- "
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3

  Y ~ X + Z + X:Z
"

fit_lms_ord <- modsem(
  model,
  data = oneIntOrd,
  method = "lms",
  ordered = colnames(oneIntOrd)
)

summary(fit_lms_ord)

modsem documentation built on June 1, 2026, 5:06 p.m.