---
title: "reliability-corrected single items"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{reliability-corrected single items}
  %\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)
```

## Reliablity-Corrected Single Items

If wanted, indicators for latent variables can be replaced with reliablity
corrected single items, using Chronbach's $\alpha$. This can either be done
using the `relcorr_single_item` function, returning the altered model syntax
and data, or via the `rcs` argument in `modsem`. Here we can see an example
using the `relcorr_single_item` function:

```{r}
tpb_uk <- "
# Outer Model (Based on Hagger et al., 2007)
 ATT =~ att3 + att2 + att1 + att4
 SN =~ sn4 + sn2 + sn3 + sn1
 PBC =~ pbc2 + pbc1 + pbc3 + pbc4
 INT =~ int2 + int1 + int3 + int4
 BEH =~ beh3 + beh2 + beh1 + beh4

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

corrected <- relcorr_single_item(syntax = tpb_uk, data = TPB_UK)
corrected
```
Here we can see that `relcorr_single_item` returns a new model syntax, and a
new `data.frame` containing the generated items. Additionally, it also
returns the Chronbach's $\alpha$ and *average variance extraced* (AVE)
for the different constructs in the model. The syntax and data can be
extracted using the `$` operator, and used to estimate the model.

```{r}
syntax <- corrected$syntax
data   <- corrected$data

est_dca <- modsem(syntax, data = data, method = "dblcent")
est_lms <- modsem(syntax, data = data, method="lms", nodes=32)
summary(est_lms)
```

The easiest approach however, is to use the `rcs` argument in the `modsem` function
to call `relcorr_single_item` before estimating the model.

```{r}
est_dca <- modsem(tpb_uk, data = TPB_UK, method = "dblcent", rcs = TRUE)
est_lms <- modsem(tpb_uk, data = TPB_UK, method = "lms", rcs = TRUE)
```

# Choosing Variables
If you don't want to use reliablity-corrected single items for all of the
latent variables in the model, you can use the `choose` argument in
`relcorr_single_item` (or`rcs.choose` in `modsem`) to select which
set of indicators to replace.

```{r}
relcorr_single_item(syntax = tpb_uk, data = TPB_UK,
                    choose = c("ATT", "SN", "PBC", "INT"))

est_dca <- modsem(tpb_uk, data = TPB_UK, method = "dblcent", rcs = TRUE,
                  rcs.choose = c("ATT", "SN", "PBC", "INT", "INT:PBC"))
summary(est_dca)
```