Plot Surface for Interaction Effects

Generates a 3D surface plot to visualize the interaction effect of two variables (x and z) on an outcome (y) using parameter estimates from a supported model object (e.g., lavaan or modsem). The function allows specifying ranges for x and z in standardized z-scores, which are then transformed back to the original scale based on their means and standard deviations.

Usage

plot_surface(
  x,
  z,
  y,
  xz = NULL,
  model,
  min_x = -3,
  max_x = 3,
  min_z = -3,
  max_z = 3,
  standardized = FALSE,
  detail = 0.01,
  ...
)

Arguments

x

A character string specifying the name of the first predictor variable.

z

A character string specifying the name of the second predictor variable.

y

A character string specifying the name of the outcome variable.

xz

Optional. A character string or vector specifying the interaction term between x and z. If NULL, the interaction term is constructed as paste(x, z, sep = ":") and adjusted for specific model classes.

model

A model object of class modsem_pi, modsem_da, modsem_mplus, or lavaan. The model should include paths for the predictors (x, z, and xz) to the outcome (y).

min_x

Numeric. Minimum value of x in z-scores. Default is -3.

max_x

Numeric. Maximum value of x in z-scores. Default is 3.

min_z

Numeric. Minimum value of z in z-scores. Default is -3.

max_z

Numeric. Maximum value of z in z-scores. Default is 3.

standardized

Should coefficients be standardized beforehand?

detail

Numeric. Step size for the grid of x and z values, determining the resolution of the surface. Smaller values increase plot resolution. Default is 1e-2.

...

Additional arguments passed to plotly::plot_ly.

Value

A plotly surface plot object displaying the predicted values of y across the grid of x and z values. The color bar shows the values of y.

Details

The input min_x, max_x, min_z, and max_z define the range of x and z values in z-scores. These are scaled by the standard deviations and shifted by the means of the respective variables, obtained from the model parameter table. The resulting surface shows the predicted values of y over the grid of x and z.

The function supports models of class modsem (with subclasses modsem_pi, modsem_da, modsem_mplus) and lavaan. For lavaan models, it is not designed for multigroup models, and a warning will be issued if multiple groups are detected.

Note

The interaction term (xz) may need to be manually specified for some models. For non-lavaan models, interaction terms may have their separator (:) removed based on circumstances.

Examples

m1 <- "
# Outer Model
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3

# Inner model
  Y ~ X + Z + X:Z
"
est1 <- modsem(m1, data = oneInt)
plot_surface("X", "Z", "Y", model = est1)


# \dontrun{
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", nodes = 32)
plot_surface(x = "INT", z = "PBC", y = "BEH", model = est2)

# }