Type:	Package
Title:	Post-Estimation Utilities for 'lavaan' Fitted Models
Version:	0.2.2
Description:	Companion toolbox for structural equation models fitted with 'lavaan'. Provides post-estimation diagnostics and graphics that operate directly on a fitted object using its estimates and covariance, and refits auxiliary models when needed. The package relies on 'lavaan' (Rosseel, 2012) <doi:10.18637/jss.v048.i02>.
URL:	https://github.com/g-corbelli/lavinteract
BugReports:	https://github.com/g-corbelli/lavinteract/issues
License:	GPL-3
Encoding:	UTF-8
Imports:	lavaan, rlang, ggplot2, stats
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown
Language:	en-US
NeedsCompilation:	no
Maintainer:	Giuseppe Corbelli <giuseppe.corbelli@uniroma1.it>
Config/testthat/edition:	3
RoxygenNote:	7.3.2
Packaged:	2025-10-31 11:08:54 UTC; giuse
Author:	Giuseppe Corbelli [aut, cre]
Repository:	CRAN
Date/Publication:	2025-11-04 11:10:02 UTC

Post-Estimation Utilities for 'lavaan' Fitted Models

Description

Companion toolbox for structural equation models fitted with 'lavaan'. Operates directly on a fitted object using its estimates and covariance. Refits auxiliary models when needed to compute estimates, diagnostics, and plots.

Details

The functions are:

• lav_slopes: simple slopes and interaction plots from a fitted 'lavaan' model.

• lav_vif: variance inflation factors for structural predictors with measurement preserved.

Note

The development of this package grew from ongoing discussions and interactions (sic) with colleagues, in particular Dr. Cataldo Giuliano Gemmano, whose steady feedback and support helped shape it.

Author(s)

Giuseppe Corbelli (<giuseppe.corbelli@uniroma1.it>, or <giuseppe.corbelli@uninettunouniversity.net>)

See Also

Useful links:

• https://github.com/g-corbelli/lavinteract

• Report bugs at https://github.com/g-corbelli/lavinteract/issues

Simple slopes and interaction plots for fitted 'lavaan' models

Description

Computes conditional (simple) slopes of a focal predictor across values of a moderator from a fitted 'lavaan' model that includes their explicit product term. Plots predicted lines with Wald confidence ribbons, and print an APA-style test of the interaction for easy reporting and interpretation, plus a simple-slopes table.

Usage

lav_slopes(
  fit,
  outcome,
  pred,
  modx,
  interaction,
  data = NULL,
  modx.values = NULL,
  modx.labels = NULL,
  pred.range = NULL,
  conf.level = 0.95,
  x.label = NULL,
  y.label = NULL,
  legend.title = NULL,
  colors = NULL,
  line.size = 0.80,
  alpha = 0.20,
  table = TRUE,
  digits = 2,
  modx_n_unique_cutoff = 4L,
  return_data = FALSE
)

## S3 method for class 'lav_slopes'
print(x, ...)

## S3 method for class 'lav_slopes'
summary(object, ...)

Arguments

Details

The model should include a main effect for the predictor, a main effect for the moderator, and their product term. The simple slope of the predictor at a given moderator value combines the predictor main effect with the interaction term. The moderator can be continuous or categorical. Standard errors use the delta method with the model covariance matrix of the estimates.

Value

A list with elements:

plot

ggplot object with lines and confidence ribbons.

slope_table

Data frame with moderator levels, simple slopes, SE, z, and CI.

plot_data

Only when return_data = TRUE: data used to build the plot.

Notes

Estimates are unstandardized; a standardized beta for the interaction is also reported for reference. Wald tests assume large-sample normality of estimates.

Examples

set.seed(42)
X <- rnorm(100); Z <- rnorm(100); X_Z <- X*Z
Y <- 0.6*X + 0.6*Z + 0.3*X_Z + rnorm(100, sd = 0.7) 
dataset <- data.frame(Y, X, Z, X_Z)
fit <- lavaan::sem("Y ~ X + Z + X_Z", data = dataset)
lav_slopes(
fit = fit, 
data = dataset,
outcome = "Y", 
pred = "X", 
modx = "Z", 
interaction = "X_Z")

Variance Inflation Factors for 'lavaan' Structural Predictors

Description

Compute VIF for each predictor that appears in structural regressions with two or more predictors, refitting the necessary sub-models so that latent predictors are handled at the latent level (i.e., with their original measurement models). It returns also the R^2 of each eligible endogenous variable from the original fit for context.

Usage

lav_vif(
  fit,
  data = NULL,
  quiet = TRUE
)

## S3 method for class 'lav_vif'
print(x, digits = 3, cutoff = c(5, 10), ...)

## S3 method for class 'lav_vif'
summary(object, ...)

Arguments

Details

Each auxiliary refitted model:

• includes the original measurement model for any latent predictors;

• includes any residual covariances among those indicators that were specified in the original model;

• regresses the focal predictor on the remaining predictors at the latent level when applicable.

VIF_i = 1 / (1 - R^2_i) generalizes VIF to SEM while respecting measurement models.

The function reuses the estimator, missing-data handling, and several options from fit.

Value

A list with:

• vif_table: data.frame with columns outcome, predictor, group, r2_predictor, vif, k_predictors.

• outcome_r2: data.frame with R^2 per eligible endogenous outcome and group from the original fit.

Examples

set.seed(42)
x1 <- rnorm(100); x2 <- 0.85*x1 + rnorm(100, sd = sqrt(1 - 0.85^2)); x3 <- rnorm(100)
y  <- 0.5*x1 + 0.3*x2 + 0.1*x3 + rnorm(100, sd = 0.7)
dataset <- data.frame(y, x1, x2, x3)
fit <- lavaan::sem("y ~ x1 + x2 + x3", data = dataset)
lav_vif(
fit = fit,
data = dataset)