---
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 = "#>"
)
```
```{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.
The x-variable is plotted on the x-axis and the y-variable is plotted on the y-axis.
And the z-variable decides at what points of z the effect of x on y is plotted.
The function will also plot the 95% confidence interval of the interaction effect.
Here we can see 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", -3:3, c(-0.2, 0), est1)
```
Here we can see 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)
```