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plotting interaction effects

plot_interactions.Rmd

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:

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:

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)

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:

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:


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)
#> Warning: Truncating SD-range on the right and left!