missing data in the LMS and QML approaches

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)

Options for Handling Missing Values

By default, missing values for both the LMS and QML are removed list-wise (missing="listwise"), such that only complete observations are kept. It is also possible to set missing="impute" to impute missing values, or to use the modsem_mimpute() function to perform multiple imputation. For the LMS approach it is also possible to use Full Information Maximum Likelihood (FIML)

Full Information Maximum Likelihood (FIML)

If you're using the LMS approach, it is possible to estimate your model using Full Information Maximum Likelihood (FIML), by setting missing="fiml". Here is an example, where we generate some missing data using the oneInt dataset.

set.seed(2834027) # set seed for reproducibility
n.missing <- 200L # generate 200 missing values
oneInt2 <- as.matrix(oneInt)
oneInt2[sample(length(oneInt2), n.missing)] <- NA

m1 <- '
# Outer Model
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3

# Inner Model
  Y ~ X + Z + X:Z
'

lms_fiml <- modsem(m1, oneInt2, method = "lms", missing = "fiml")
summary(lms_fiml)

(Multiple) Imputation

If you're using the QML approach it is not possible (yet) to use FIML, and FIML can also be very computationally intensive when using the LMS approach. Another option is to use (multiple) imputation.

If missing="impute" is set, single imputation is performed.

qml_impute <- modsem(m1, oneInt2, method = "qml", missing = "impute")
summary(qml_impute)

A better option than single imputation, is multiple imputation, which can be performed both for the LMS and QML approaches, using the modsem_mimpute() function.

lms_mimpute <- modsem_mimpute(m1, oneInt2, method = "lms")
summary(lms_mimpute)

modsem documentation built on Aug. 27, 2025, 9:08 a.m.