R: Plot the graphs for probing latent interaction

plotProbe {semTools} | R Documentation

## Plot the graphs for probing latent interaction

### Description

This function will plot the line graphs representing the simple effect of the independent variable given the values of the moderator.

### Usage

    plotProbe(object, xlim, xlab="Indepedent Variable", ylab="Dependent Variable", ...)

### 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
`...` | Any addition argument for the `plot` function

### Value

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

### Author(s)

Sunthud Pornprasertmanit (psunthud@gmail.com)

### 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, std.lv=FALSE)
    result2wayMC <- probe2WayMC(fitMC2way, c("f1", "f2", "f12"), "f3", "f2", 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, meanstructure=TRUE, std.lv=FALSE)
    result3wayMC <- probe3WayMC(fitMC3way, c("f1", "f2", "f3", "f12", "f13", "f23", "f123"),
    	"f4", c("f1", "f2"), c(-1, 0, 1), c(-1, 0, 1))
    plotProbe(result3wayMC, xlim=c(-2, 2))

[Package semTools version 0.4-14 Index]