如果您正在寻找这种（元分析）类型的漏斗图，那么以下可能是一个起点：

library(ggplot2)

set.seed(1)
p <- runif(100)
number <- sample(1:1000, 100, replace = TRUE)
p.se <- sqrt((p*(1-p)) / (number))
df <- data.frame(p, number, p.se)

## common effect (fixed effect model)
p.fem <- weighted.mean(p, 1/p.se^2)

## lower and upper limits for 95% and 99.9% CI, based on FEM estimator
number.seq <- seq(0.001, max(number), 0.1)
number.ll95 <- p.fem - 1.96 * sqrt((p.fem*(1-p.fem)) / (number.seq)) 
number.ul95 <- p.fem + 1.96 * sqrt((p.fem*(1-p.fem)) / (number.seq)) 
number.ll999 <- p.fem - 3.29 * sqrt((p.fem*(1-p.fem)) / (number.seq)) 
number.ul999 <- p.fem + 3.29 * sqrt((p.fem*(1-p.fem)) / (number.seq)) 
dfCI <- data.frame(number.ll95, number.ul95, number.ll999, number.ul999, number.seq, p.fem)

## draw plot
fp <- ggplot(aes(x = number, y = p), data = df) +
    geom_point(shape = 1) +
    geom_line(aes(x = number.seq, y = number.ll95), data = dfCI) +
    geom_line(aes(x = number.seq, y = number.ul95), data = dfCI) +
    geom_line(aes(x = number.seq, y = number.ll999), linetype = "dashed", data = dfCI) +
    geom_line(aes(x = number.seq, y = number.ul999), linetype = "dashed", data = dfCI) +
    geom_hline(aes(yintercept = p.fem), data = dfCI) +
    scale_y_continuous(limits = c(0,1.1)) +
  xlab("number") + ylab("p") + theme_bw() 
fp

虽然还有改进的余地，但这里有一个模拟（异方差）数据的小尝试：

library(ggplot2)
set.seed(101)
x <- runif(100, min=1, max=10)
y <- rnorm(length(x), mean=5, sd=0.1*x)
df <- data.frame(x=x*70, y=y)
m <- lm(y ~ x, data=df) 
fit95 <- predict(m, interval="conf", level=.95)
fit99 <- predict(m, interval="conf", level=.999)
df <- cbind.data.frame(df, 
                       lwr95=fit95[,"lwr"],  upr95=fit95[,"upr"],     
                       lwr99=fit99[,"lwr"],  upr99=fit99[,"upr"])

p <- ggplot(df, aes(x, y)) 
p + geom_point() + 
    geom_smooth(method="lm", colour="black", lwd=1.1, se=FALSE) + 
    geom_line(aes(y = upr95), color="black", linetype=2) + 
    geom_line(aes(y = lwr95), color="black", linetype=2) +
    geom_line(aes(y = upr99), color="red", linetype=3) + 
    geom_line(aes(y = lwr99), color="red", linetype=3)  + 
    annotate("text", 100, 6.5, label="95% limit", colour="black", 
             size=3, hjust=0) +
    annotate("text", 100, 6.4, label="99.9% limit", colour="red", 
             size=3, hjust=0) +
    labs(x="No. admissions...", y="Percentage of patients...") +    
    theme_bw() 

Bernd Weiss 的代码很有帮助。我在下面做了一些修改，以更改/添加一些功能：

1. 使用标准误差作为精度的度量，这是我看到的更典型的漏斗图（在心理学中）
2. 交换轴，因此精度（标准误差）在 y 轴上，效果大小在 x 轴上
3. 用于geom_segment代替geom_line划分元分析平均值的线，使其与划分 95% 和 99% 置信区域的线高度相同
4. 我没有绘制元分析平均值，而是绘制了它的 95% 置信区间

我的代码使用 0.0892 (se = 0.0035) 的元分析平均值作为示例，但您可以替换自己的值。

estimate = 0.0892
se = 0.0035

#Store a vector of values that spans the range from 0
#to the max value of impression (standard error) in your dataset.
#Make the increment (the final value) small enough (I choose 0.001)
#to ensure your whole range of data is captured
se.seq=seq(0, max(dat$corr_zi_se), 0.001)

#Compute vectors of the lower-limit and upper limit values for
#the 95% CI region
ll95 = estimate-(1.96*se.seq)
ul95 = estimate+(1.96*se.seq)

#Do this for a 99% CI region too
ll99 = estimate-(3.29*se.seq)
ul99 = estimate+(3.29*se.seq)

#And finally, calculate the confidence interval for your meta-analytic estimate 
meanll95 = estimate-(1.96*se)
meanul95 = estimate+(1.96*se)

#Put all calculated values into one data frame
#You might get a warning about '...row names were found from a short variable...' 
#You can ignore it.
dfCI = data.frame(ll95, ul95, ll99, ul99, se.seq, estimate, meanll95, meanul95)


#Draw Plot
fp = ggplot(aes(x = se, y = Zr), data = dat) +
  geom_point(shape = 1) +
  xlab('Standard Error') + ylab('Zr')+
  geom_line(aes(x = se.seq, y = ll95), linetype = 'dotted', data = dfCI) +
  geom_line(aes(x = se.seq, y = ul95), linetype = 'dotted', data = dfCI) +
  geom_line(aes(x = se.seq, y = ll99), linetype = 'dashed', data = dfCI) +
  geom_line(aes(x = se.seq, y = ul99), linetype = 'dashed', data = dfCI) +
  geom_segment(aes(x = min(se.seq), y = meanll95, xend = max(se.seq), yend = meanll95), linetype='dotted', data=dfCI) +
  geom_segment(aes(x = min(se.seq), y = meanul95, xend = max(se.seq), yend = meanul95), linetype='dotted', data=dfCI) +
  scale_x_reverse()+
  scale_y_continuous(breaks=seq(-1.25,2,0.25))+
  coord_flip()+
  theme_bw()
fp

另请参阅 cran 包 berryFunctions，如果有人在基本图形中需要它，它有一个不使用 ggplot2 的比例漏斗图。 http://cran.r-project.org/web/packages/berryFunctions/index.html

还有extfunnel这个包，我没看过。