ggplot2：为泊松零膨胀模型创建置信区间（IC）

Question

我想使用pscl包为泊松零膨胀模型（IC 95％）创建一个具有置信区间（IC）的图。此套件不计算IC，而是使用以下功能：

predict.zeroinfl <- function(object, newdata, type = c("response", "prob"),
                             se=FALSE,MC=1000,level=.95,na.action = na.pass, ...){
  type <- match.arg(type)

  ## if no new data supplied
  if(missing(newdata)){
    rval <- object$fitted.values
    if(!is.null(object$x)) {
      X <- object$x$count
      Z <- object$x$zero
    }
    else if(!is.null(object$model)) {
      X <- model.matrix(object$terms$count, object$model, 
                        contrasts = object$contrasts$count)
      Z <- model.matrix(object$terms$zero,  object$model, 
                        contrasts = object$contrasts$zero)
    }
    else {
      stop("no X and/or Z matrices can be extracted from fitted model")
    }
    if(type == "prob") {
      mu <- exp(X %*% object$coefficients$count)[,1]
      phi <- object$linkinv(Z %*% object$coefficients$zero)[,1]
    }    
    else {
      mf <- model.frame(delete.response(object$terms$full), newdata, 
                        na.action = na.action, xlev = object$levels)
      X <- model.matrix(delete.response(object$terms$count), mf, 
                        contrasts = object$contrasts$count)
      Z <- model.matrix(delete.response(object$terms$zero),  mf, 
                        contrasts = object$contrasts$zero)    
      mu <- exp(X %*% object$coefficients$count)[,1]
      phi <- object$linkinv(Z %*% object$coefficients$zero)[,1]
      rval <- (1-phi) * mu
    }   
    if(se & !is.null(X) & !is.null(Z)){
      require(mvtnorm)
      vc <- -solve(object$optim$hessian)
      kx <- length(object$coefficients$count)
      kz <- length(object$coefficients$zero)
      parms <- object$optim$par

      if(type!="prob"){
        yhat.sim <- matrix(NA,MC,dim(X)[1])
        for(i in 1:MC){
          cat(paste("MC iterate",i,"of",MC,"\n"))
          parms.sim <- rmvnorm(n=1,mean=parms,sigma=vc)
          beta <- parms.sim[1:kx]
          gamma <- parms.sim[(kx+1):(kx+kz)]
          mu.sim <- exp(X%*%beta)[,1]
          phi.sim <- object$linkinv(Z%*%gamma)[,1]
          yhat.sim[i,] <- (1-phi.sim)*mu.sim
        }
      }

      out <- list()
      out$lower <- apply(yhat.sim,2,quantile,(1-level)/2)
      out$upper <- apply(yhat.sim,2,quantile,1-((1-level)/2))
      out$se <- apply(yhat.sim,2,sd)
    }
    ## predicted probabilities
    if(type == "prob") {
      if(!is.null(object$y)) 
        y <- object$y
      else if(!is.null(object$model)) 
        y <- model.response(object$model)
      else 
        stop("predicted probabilities cannot be computed for fits with 
              y = FALSE and model = FALSE")
      yUnique <- min(y):max(y)
      nUnique <- length(yUnique)
      rval <- matrix(NA, nrow = length(rval), ncol = nUnique)
      dimnames(rval) <- list(rownames(X), yUnique)
      switch(object$dist,
             "poisson" = {
               rval[, 1] <- phi + (1-phi) * exp(-mu)
               for(i in 2:nUnique) 
                 rval[,i] <- (1-phi) * dpois(yUnique[i], lambda = mu)
             },
             "negbin" = {
               theta <- object$theta
               rval[, 1] <- phi + (1-phi) * dnbinom(0, mu = mu, size = theta)
               for(i in 2:nUnique) 
                  rval[,i] <- (1-phi) * dnbinom(yUnique[i], mu = mu, size = theta)    
             },
             "geometric" = {
               rval[, 1] <- phi + (1-phi) * dnbinom(0, mu = mu, size = 1)
               for(i in 2:nUnique) 
                  rval[,i] <- (1-phi) * dnbinom(yUnique[i], mu = mu, size = 1)            
             })    
    }

    if(se)
      rval <- list(rval,out)
    rval
}

要尝试绘制我得出的结果：

#Packages
require(ggplot2)
require(pscl)

#Data set
zinb <- read.csv("https://stats.idre.ucla.edu/stat/data/fish.csv")
zinb <- within(zinb, {
    nofish <- factor(nofish)
    livebait <- factor(livebait)
    camper <- factor(camper)
})


#Create zero inflated poisson model
m1 <- zeroinfl(count ~ child + camper | persons, data = zinb)
summary(m1)


# Create the predition and confidence interval by bootstrap
dc_F<-NULL
pred <-predict.zeroinfl(m1,se=TRUE,type="response") ### Function
dc_F= cbind(dc_F, pred = pred[[1]])
dc_F = cbind(dc_F, ucl = pred[[2]]$upper) 
dc_F = cbind(dc_F, lcl= pred[[2]]$lower) 
dc_F<-as.data.frame(dc_F)

#Plot poisson part
ggplot(data=zinb, mapping=aes(x=child, y= count, color=camper)) + 
  geom_point() +  
  geom_line(data=dc_F, mapping=aes(y=pred,x=zinb$child)) +
  geom_smooth(data=dc_F, aes(ymin = lcl, ymax = ucl), stat="identity")

任何成员都可以帮助我给zinb（观察到的数据集）和dc_F结果（通过引导程序预测和置信区间）提供相同的大小吗？

谢谢！