plotProbe {semTools}

Plot a latent interaction

Description

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.

Usage

plotProbe(object, xlim, xlab = "Indepedent Variable",
  ylab = "Dependent Variable", legend = TRUE, legendArgs = list(), ...)

Arguments

object

The result of probing latent interaction obtained from probe2WayMC, probe2WayRC, probe3WayMC, or probe3WayRC function.

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

logical. If TRUE (default), a legend is printed.

legendArgs

list of arguments passed to legend function if legend=TRUE.

...

Any addition argument for the plot function

Value

None. This function will plot the simple main effect only.

Author(s)

Sunthud Pornprasertmanit ([e-mail])

Terrence D. Jorgensen (University of Amsterdam; [e-mail])

References

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

See Also

• 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.

Examples

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))