plotProbe {semTools} | R Documentation |
This function will plot the line graphs representing the simple effect of the independent variable given the values of the moderator. For multigroup models, it will only generate a plot for 1 group, as specified in the function used to obtain the first argument.
plotProbe(object, xlim, xlab = "Indepedent Variable",
ylab = "Dependent Variable", legend = TRUE, legendArgs = list(), ...)
object |
The result of probing latent interaction obtained from
|
xlim |
The vector of two numbers: the minimum and maximum values of the independent variable |
xlab |
The label of the x-axis |
ylab |
The label of the y-axis |
legend |
|
legendArgs |
|
... |
Any addition argument for the |
None. This function will plot the simple main effect only.
Sunthud Pornprasertmanit (psunthud@gmail.com)
Terrence D. Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)
Schoemann, A. M., & Jorgensen, T. D. (2021). Testing and interpreting
latent variable interactions using the semTools
package.
Psych, 3(3), 322–335. doi:10.3390/psych3030024
indProd
For creating the indicator products with no
centering, mean centering, double-mean centering, or residual centering.
probe2WayMC
For probing the two-way latent interaction
when the results are obtained from mean-centering, or double-mean centering
probe3WayMC
For probing the three-way latent interaction
when the results are obtained from mean-centering, or double-mean centering
probe2WayRC
For probing the two-way latent interaction
when the results are obtained from residual-centering approach.
probe3WayRC
For probing the two-way latent interaction
when the results are obtained from residual-centering approach.
library(lavaan)
dat2wayMC <- indProd(dat2way, 1:3, 4:6)
model1 <- "
f1 =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
f12 =~ x1.x4 + x2.x5 + x3.x6
f3 =~ x7 + x8 + x9
f3 ~ f1 + f2 + f12
f12 ~~ 0*f1
f12 ~~ 0*f2
x1 ~ 0*1
x4 ~ 0*1
x1.x4 ~ 0*1
x7 ~ 0*1
f1 ~ NA*1
f2 ~ NA*1
f12 ~ NA*1
f3 ~ NA*1
"
fitMC2way <- sem(model1, data = dat2wayMC, meanstructure = TRUE)
result2wayMC <- probe2WayMC(fitMC2way, nameX = c("f1", "f2", "f12"),
nameY = "f3", modVar = "f2", valProbe = c(-1, 0, 1))
plotProbe(result2wayMC, xlim = c(-2, 2))
dat3wayMC <- indProd(dat3way, 1:3, 4:6, 7:9)
model3 <- "
f1 =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
f3 =~ x7 + x8 + x9
f12 =~ x1.x4 + x2.x5 + x3.x6
f13 =~ x1.x7 + x2.x8 + x3.x9
f23 =~ x4.x7 + x5.x8 + x6.x9
f123 =~ x1.x4.x7 + x2.x5.x8 + x3.x6.x9
f4 =~ x10 + x11 + x12
f4 ~ f1 + f2 + f3 + f12 + f13 + f23 + f123
f1 ~~ 0*f12
f1 ~~ 0*f13
f1 ~~ 0*f123
f2 ~~ 0*f12
f2 ~~ 0*f23
f2 ~~ 0*f123
f3 ~~ 0*f13
f3 ~~ 0*f23
f3 ~~ 0*f123
f12 ~~ 0*f123
f13 ~~ 0*f123
f23 ~~ 0*f123
x1 ~ 0*1
x4 ~ 0*1
x7 ~ 0*1
x10 ~ 0*1
x1.x4 ~ 0*1
x1.x7 ~ 0*1
x4.x7 ~ 0*1
x1.x4.x7 ~ 0*1
f1 ~ NA*1
f2 ~ NA*1
f3 ~ NA*1
f12 ~ NA*1
f13 ~ NA*1
f23 ~ NA*1
f123 ~ NA*1
f4 ~ NA*1
"
fitMC3way <- sem(model3, data = dat3wayMC, std.lv = FALSE,
meanstructure = TRUE)
result3wayMC <- probe3WayMC(fitMC3way, nameX = c("f1", "f2", "f3", "f12",
"f13", "f23", "f123"),
nameY = "f4", modVar = c("f1", "f2"),
valProbe1 = c(-1, 0, 1), valProbe2 = c(-1, 0, 1))
plotProbe(result3wayMC, xlim = c(-2, 2))