---
title: "composite constructs"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{composite constructs}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```{r setup}
library(modsem)
```

# Composite Constructs
As of version `1.0.20`, the `modsem` function supports the estimation of models
with composite constructs, when using `method="lms"`. The approach is based
on [Tamara Schamberger, Florian Schuberth, Jörg Henseler & Yves Rosseel, 2015](https://arxiv.org/abs/2508.06112).
Depending on your `lavaan` version (`>=0.6-20`), composite constructs can also
be used with the product indicator approaches (e.g., `method="dblcent"`).

Here we can see a simple example, using the LMS approach with the `TPB` dataset.

```{r}
tpb <- '
# Outer Model (Based on Hagger et al., 2007)
  # Latent Variables
  SN =~ sn1 + sn2
  PBC =~ pbc1 + pbc2 + pbc3
  INT =~ int1 + int2 + int3

  # Composites
  ATT <~ att1 + att2 + att3 + att4 + att5
  BEH <~ b1 + b2

# Inner Model (Based on Steinmetz et al., 2011)
  INT ~ ATT + SN + PBC
  BEH ~ INT + PBC + INT:PBC
'

fit <- modsem(tpb, TPB, method = "lms", nodes = 32)
summary(fit)
```