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.

```
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.

```
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")
#> Warning: It is recommended that you have at least 32 nodes for interaction
#> effects between exogenous and endogenous variables in the lms approach 'nodes =
#> 24'
plot_interaction(x = "INT", z = "PBC", y = "BEH", xz = "PBC:INT",
vals_z = c(-0.5, 0.5), model = est2)
```