R Code Lavaan SEM Moderated-Mediation

#Centering IV and Mediator
X.C <- scale(X, center = TRUE, scale = FALSE)[,]
M.C <- scale(M, center = TRUE, scale = FALSE)[,]

#Centering Moderators
W.C <- scale(W, center = TRUE, scale = FALSE)[,]

#Identifying values for simple slopes of moderator
quantile(W, probs = c(0.16, 0.5, 0.84))
summary(W)

#Set Moderator interaction terms
XcxWc <- X.C*W.C #Interaction term, IV and moderator
McxWc <- M.C*W.C #Interaction term, Mediator and moderator

ModMed_Data <- data.frame (X.C, Group, M.C, W.C, XcxWc) #dataframe for model
lapply(ModMed_Data[,c("Group")], ordered) #ensure that lavaan recognises Group as an endogenous ordinal variable 

ModeratedMediationModel_Defined <-
    ' #Regressions
      Group ~ b1*M.C + b2*McxWc + cdash1*X.C +cdash2*W.C + cdash3*XcxWc
      M.C ~ a1*X.C + a2*W.C +a3*XcxWc

      #Simple slope of M on X (a1+a3*W). Values in brackets derived from values the moderator obtained with quantile/summary etc functions above
      aSDbelow := a1+a3*(W)
      aLow := a1+a3*(W)
      aMedian := a1+a3*(W)
      aMean := a1+a3*(0)
      aSDabove := a1+a3*(W)
      aHigh := a1+a3*(W)
      
      #Simple slope of Y on M (b1+b2*W).       
      bSDbelow := b1+b2*(W)
      bLow := b1+b2*(W)
      bMedian := b1+b2*(W)
      bMean := b1+b2*(0)
      bSDabove := b1+b2*(W)
      bHigh := b1+b2*(W)

      #Indirect effects conditional on moderator (a1 + a3)*(b1+ b2)
      abSDbelow := bSDbelow*aSDbelow
      abLow: = bLow*aLow
      abMedian: = bMedian*aMedian
      abMean: = bMean*aMean
      abSDabove := bSDabove*aSDabove
      abHigh: = bHigh*aHigh
      
      #Direct effects conditional on moderator (cdash1 + cdash3)*W
      cSDbelow := cdash1+cdash3*(W)
      cLow := cdash1+cdash3*(W)
      cMedian := cdash1+cdash3*(W)
      cMean := cdash1+cdash3*(0)
      cSDabove := cdash1+cdash3*(W)
      cHigh := cdash1+cdash3*(W)
      
      #Total effects conditional on moderator
      total.SDbelow := cSDbelow + abSDbelow
      total.Low := clow + ablow
      total.Median := cMedian + abMedian
      total.Mean := cMean + abMean
      total.SDabove := cSDabove + abSDabove
      total.High := cHigh + abHigh

      #Proportion mediated conditional on moderator
      #To match the output of "mediate" package
      prop.mediated.SDbelow := abSDbelow / total.SDbelow
      prop.mediated.Low := abLow / total.Low
      prop.mediated.Median := abMedian / total.Median
      prop.mediated.Mean := abMean / total.Mean
      prop.mediated.SDabove := abSDabove / total.SDabove
      prop.mediated.High := abHigh / total.High

      #Index of moderated mediation
      index.mod.med1 := a3*b2
      index.mod.med2 := a1*b2+a3*b1
'

ModeratedMediationModel <- sem(model = ModeratedMediationModel_Defined, data = ModMed_Data, estimator = "WLSMV", bootstrap = 10000)
Summary(ModeratedMediationModel, fit.measures = TRUE, standardize = TRUE, rsquare = TRUE)
paramaterEstimates(ModeratedMediationModel, boot.ci.type = "bca.simple", level =.95, ci = TRUE, standardized = FALSE)