Plot Interaction Effects
plot_interaction.Rd
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
andz
.- vals_x
The values of the
x
variable to plot, the more values the smoother the std.error-area will be. NOTE:vals_x
are measured relative to the mean ofx
. The correct values will show up in the plot.- vals_z
The values of the moderator variable to plot. A separate regression NOTE:
vals_z
are measured relative to the mean ofz
. The correct values will show up in the plot. line (y ~ x | z
) will be plotted for each value of the moderator variable- model
An object of class
modsem_pi
,modsem_da
, ormodsem_mplus
- alpha_se
The alpha level for the std.error area
- ...
Additional arguments passed to other functions
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)
} # }