Using ggpredict after feglm

Question

I want to make a fixed-effects glm model with feglm and want to use ggpredict afterward. Yet, somehow I always get errors (see below). Is it possible to use ggpredict after feglm at all, or am I doing something wrong here?

# Make example data set
    
    obsnum <- c(1:8000)
    DF <- data.frame(obsnum)  # Dataset with 8000 observations
    
    DF$help1 <- 0  
    DF$help1[((DF$obsnum-1) %% 80) == 0] <- 1
    
    DF$participantID  <- cumsum(DF$help1)             # Make participant var (cluster)
    DF$itemNr <- data.table::rowid(DF$participantID)  # ordered item numbers within participants
    DF$V1 <- stats::rbinom(8000, 1, 0.5)
    DF$V2 <- stats::rbinom(8000, 1, 0.5)
    DF$V3 <- sample(1:6, 8000, replace = TRUE)
    
    
    # Create random effect
    
    DF$clustRandEffect <- rep(rnorm(100,0,0.05),each=80) 
    
    # First make percent with intercept  ####
        p_0 <- 0.7
        p_V1 <- p_0 + 0.1
        p_V2 <- p_0 - 0.1
        p_V1V2 <-  p_0 + 0.05
        p_V3 <- p_0 + 0.05
        
    # Then logs of odds ratios ####
    
        fu_odds <- function(p) {
          odds <- p/(1-p)
          return (odds)
        }
        
        fu_oddR <- function(o){
          oR <- o/fu_odds(p_0)
          return(oR)
        }
        
        b0 <- log(fu_oddR(fu_odds(p_0)))
        b_V1 <- log(fu_oddR(fu_odds(p_V1)))
        b_V2 <- log(fu_oddR(fu_odds( p_V2)))
        b_V1V2 <- log(fu_oddR(fu_odds(p_V1V2)))
        b_V2 <- log(fu_oddR(fu_odds(p_V3)))
        
    # y probability
      DF$prop = stats::plogis((b0+b_V1*DF$V1+b_V2*DF$V2
                     +b_V1V2*DF$V1*DF$V2 + b_V2*DF$V3 + DF$clustRandEffect))
    
    # Make Y  ####
      DF$Y <- rbinom(n= 8000, size = 1,  DF$prop)
      

# Make model 

    # Version 1
  
        model1 <- fixest::feglm(Y ~ V1 * V2 + V3 | participantID + itemNr, DF)
        
          
        ggeffects::ggpredict(
                    model = model1,
                    terms = c("V1", "V2"),
                    cluster = DF$participantID, # Same error with or without cluster setting
                    ci.lvl = 0.90 
                    )  #Error in tapply(x$predicted, list(x$group), NULL) :   Argumente müssen die selbe Länge haben
        
        # Yet all have the same length
        length(DF$Y)    # 8000
        length(DF$V1)   # 8000
        length(DF$V2)    # 8000
      
    # Version 2
  
        model2 <- fixest::feglm(Y ~ V1 * V2 + V3 | participantID, DF)
        
          
        ggeffects::ggpredict(
                    model = model2,
                    terms = c("V1", "V2"),
                    cluster = DF$participantID,
                    ci.lvl = 0.90 
                    )  # Error in tapply(x$predicted, list(x$group), NULL) :   Argumente müssen die selbe Länge haben
        
        
        ggeffects::ggpredict(
            model = summary(model2),
            terms = c("V1", "V2"),
            cluster = DF$participantID,
            ci.lvl = 0.90 
            )  # Error in tapply(x$predicted, list(x$group), NULL) :   Argumente müssen die selbe Länge haben
        
    # Version 3
  
        model3 <- fixest::feglm(Y ~ 0 + V1 * V2 + V3 | participantID, DF)
        
          
        ggeffects::ggpredict(
                    model = model3,
                    terms = c("V1", "V2"),
                    cluster = DF$participantID,
                    ci.lvl = 0.90 
                    )  # Error in tapply(x$predicted, list(x$group), NULL) :   Argumente müssen die selbe Länge haben
        
    # Version 4 - Set DF as data frame again
        DF <- as.data.frame(DF)
        class(DF)
        model4 <- fixest::feglm(Y ~  V1 * V2 + V3 | participantID, DF)
        
          
        ggeffects::ggpredict(
                    model = model4,
                    terms = c("V1", "V2"),
                    # cluster = DF$participantID,
                    ci.lvl = 0.90 
                    )  # Error in tapply(x$predicted, list(x$group), NULL) :   Argumente müssen die selbe Länge haben
        

PS: I have asked a similar question on ggpredict after bife here.