R Funktion selber schreiben -> Moderationsanalyse - r

Question

I'm trying to create my first function in R to calculate moderation analysis.
But now a problem accured I cannot solve :/
When I run my function, I don't get any output..
I also tried print() and return() with the same result.
Any recommendations?
Moderation <- function(Mod, UV, AV) {
meanUV <- mean(UV, na.rm = TRUE)
sdUV <- sd(UV, na.rm = TRUE)
ZUV <-((UV - meanUV)/sdUV)
meanMod <- mean(Mod, na.rm = TRUE)
sdMod <- sd(Mod, na.rm = TRUE)
ZMod <- (Mod - meanMod)/sdMod
Interaktion <- ZUV*ZAV
Moderation.fit <- 'AV ~ ZUV + ZMod + Interaktion'
summary(sem(model = Moderation.fit, data = MyData, meanstructure = TRUE))
}
Moderation(MyData$SKK, MyData$ZDT2, MyData$HO4)
Thank you for your help!

Answer 1

The problem in your function is that you refer to MyData in the last line even though you do not pass this dataset to the function. Actually, the function does look into your global environment and finds MyData there. But your z-scored variables and Interaktion are not included in this version of MyData, so the function reports an error about "missing variables in dataset".
It works with the following changes:
Moderation <- function(Mod, UV, AV) {
require(lavaan) #This ensures the function works even if you did not do library(lavaan) first.
ZMod <- scale(Mod) #I simplified stuff here. The "scale" function is equivalent to what you did.
ZUV <- scale(UV)
Interaktion <- ZUV*ZMod
MyData <- as.data.frame(list(ZMod=ZMod, ZUV=ZUV, AV=AV, Interaktion=Interaktion)) #Here, MyData is generated so that you can use it further below
Moderation.fit <- 'AV ~ ZUV + ZMod + Interaktion' #nothing changed here
summary(sem(model = Moderation.fit, data = MyData, meanstructure = TRUE))
}
I tested it with the popular mtcars dataset:
data(mtcars)
Moderation(mtcars$am, mtcars$hp, mtcars$mpg)
##lavaan 0.6-7 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 5
##
## Number of observations 32
##
##Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
##Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##Regressions:
## Estimate Std.Err z-value P(>|z|)
## AV ~
## ZUV -4.043 0.560 -7.225 0.000
## ZMod 2.633 0.513 5.134 0.000
## Interaktion 0.014 0.527 0.026 0.979
##
##Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .AV 20.094 0.505 39.785 0.000
##
##Variances:
## Estimate Std.Err z-value P(>|z|)
## .AV 7.670 1.917 4.000 0.000
Viel Erfolg und Spass damit!