# simple slopes analysis

    library(modsem)

# 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)