1

I wasn't sure whether this was more appropriate to ask here or CrossValidated as I'm specifically asking about using R / lavaan...

I'm not sure if I've completely misunderstood how violations of assumptions are checked. I understand that we can obtain diagnostic plots for linear models with:

model <- lm(data$outcome ~ data$predictor)

plot(model, which = c(1:6))

But I'm having trouble figuring out how I should do this for a mediation model fitted like so:

model <- 'outcome ~ c*predictor + b*mediator
              mediator ~ a*predictor
              indirect_effect := a*b
              total_effect := c + (a*b)
              '

model.fit <- lavaan::sem(
  model = model,
  data = data,
  missing = "FIML",
  estimator = "ML")

Then if I try obtaining plots in the same way (plot(model.fit, which = c(1:6))), I get Error in as.double(y) : cannot coerce type 'S4' to vector of type 'double'.

Also, to check for violations of assumptions for Pearson's correlation, would we do so by looking at the structure of each variable individually, or by making a linear model (lm(data$outcome ~ data$predictor)), or using the correlation itself (cor.test(data$var1, data$var2)) in some way?

user13343754
  • 91
  • 5

1 Answers1

1

Try:

lavaanPlot::lavaanPlot(model = model.fit, coefs=T)
Bram
  • 2,515
  • 6
  • 36
  • 58
pAo
  • 11
  • 2