Plot Interaction Effects

Usage

plot_interaction(
  x,
  z,
  y,
  xz = NULL,
  vals_x = seq(-3, 3, 0.001),
  vals_z,
  model,
  alpha_se = 0.15,
  ...
)

Arguments

x: The name of the variable on the x-axis
z: The name of the moderator variable
y: The name of the outcome variable
xz: The name of the interaction term. If the interaction term is not specified, it will be created using x and z.
vals_x: The values of the x variable to plot, the more values the smoother the std.error-area will be
vals_z: The values of the moderator variable to plot. A separate regression line (y ~ x | z) will be plotted for each value of the moderator variable
model: An object of class modsem_pi, modsem_da, or modsem_mplus
alpha_se: The alpha level for the std.error area
...: Additional arguments passed to other functions

Value

A ggplot object

Examples

library(modsem)
if (FALSE) { # \dontrun{
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)

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)
  # Causal Relationsships
  INT ~ ATT + SN + PBC
  BEH ~ INT + PBC
  # BEH ~ ATT:PBC
  BEH ~ PBC:INT
  # BEH ~ PBC:PBC
"

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)
} # }