---
title: "plotting interaction effects"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{plotting interaction effects}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

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

# Plotting Interaction Effects

Interaction effects can be plotted using the included `plot_interaction()` function. This function takes a fitted model object and the names of the two variables that are interacting. The function will plot the interaction effect of the two variables, where:

- The x-variable is plotted on the x-axis.
- The y-variable is plotted on the y-axis.
- The z-variable determines at which points the effect of x on y is plotted.
  
The function will also plot the 95% confidence interval for the interaction effect.

Here is a simple example using the double-centering approach:

```{r}
m1 <- "
# Outer Model
  X =~ x1
  X =~ x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3

# Inner Model
  Y ~ X + Z + X:Z
"
est1 <- modsem(m1, data = oneInt)
plot_interaction("X", "Z", "Y", "X:Z", vals_z = -3:3, range_y = c(-0.2, 0), model = est1)
```

Here is a different example using the `lms` approach in the theory of planned behavior model:

```{r}
tpb <- "
# Outer Model (Based on Hagger et al., 2007)
  ATT =~ att1 + att2 + att3 + att4 + att5
  SN =~ sn1 + sn2
  PBC =~ pbc1 + pbc2 + pbc3
  INT =~ int1 + int2 + int3
  BEH =~ b1 + b2

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

est2 <- modsem(tpb, TPB, method = "lms")
plot_interaction(x = "INT", z = "PBC", y = "BEH", xz = "PBC:INT", 
                 vals_z = c(-0.5, 0.5), model = est2)
```

# Plotting Johnson-Neyman Regions

The `plot_jn()` function can be used to plot Johnson-Neyman regions for a given interaction effect. 
This function takes a fitted model object, the names of the two variables that are interacting, 
and the name of the interaction effect. The function will plot the Johnson-Neyman regions for the interaction effect.

The `plot_jn()` function will also plot the 95% confidence interval for the interaction effect.

`x` is the name of the x-variable, `z` is the name of the z-variable, and `y` is the name of the y-variable.
`model` is the fitted model object. The argument `min_z` and `max_z` are used to specify the range of values for the 
moderating variable.

Here is an example using the `ca` approach in the Holzinger-Swineford (1939) dataset:

```{r}
m1 <-  ' 
  visual  =~ x1 + x2 + x3 
  textual =~ x4 + x5 + x6
  speed   =~ x7 + x8 + x9

  visual ~ speed + textual + speed:textual
'

est <- modsem(m1, data = lavaan::HolzingerSwineford1939, method = "ca")
plot_jn(x = "speed", z = "textual", y = "visual", model = est, max_z = 6)
```

Here is another example using the `qml` approach in the theory of planned behavior model:
```{r}

tpb <- "
# Outer Model (Based on Hagger et al., 2007)
  ATT =~ att1 + att2 + att3 + att4 + att5
  SN =~ sn1 + sn2
  PBC =~ pbc1 + pbc2 + pbc3
  INT =~ int1 + int2 + int3
  BEH =~ b1 + b2

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

est2 <- modsem(tpb, TPB, method = "qml")
plot_jn(x = "INT", z = "PBC", y = "BEH", model = est2,
                    min_z = -1.5, max_z = -0.5)
```

