Simple Slopes Analysis

Simple slope 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. Note that the vals_z argument (as well as the values of x) are scaled by the mean and standard deviation of the variables. Unless the rescale argument is set to FALSE.

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 = "X", z = "Z", y = "Y", vals_z = c(-1, 1), model = est1)

If you want to see the numerical values of the simple slopes, you can use the simple_slopes() function:

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)
simple_slopes(x = "X", z = "Z", y = "Y", vals_z = c(-1, 1), model = est1)

The simple_slopes() function returns a simple_slopes object. It only has two methods/generics: print.simple_slopes(), which prints the simple slopes in a easy-to-read format and as.data.frame.simple_slopes(). The print() method will not only print the predicted values, but also significance tests for the difference between the slope at the lowest value of z and the slope at the highest value of z, as well as significance tests for the slope of x at the different values of vals_z.

In the example above, we can see that there is a significant difference between the slope at at -1 * sd(Z) and +1 * sd(Z). Note that by default vals_z is rescaled by the mean and standard deviation of the variable, unless rescale = FALSE is set. This means that the values of vals_z are interpreted as standard deviations from the mean of Z.

If you want to extract the simple slopes as a data.frame, you can use the as.data.frame() function:

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)
slopes <- simple_slopes(x = "X", z = "Z", y = "Y", 
                        vals_z = c(0, 1), model = est1)
as.data.frame(slopes)

simple slopes analysis

Simple Slopes Analysis