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I have a problem where I need to calculate a confidence interval for a ratio of two sums. I have code below which calculates this stat using a bootstrapping method, but I would much prefer a closed form formula as I have this in a R Shiny app and it would react much faster.

Below I have code that calculates a bootstrapped confidence interval for the sum(Sepal.Length)/sum(Sepal.Width) by Species using the iris dataset in R.

Any ideas on a closed form solution for this problem? I feel like I am missing something obvious.

Set up data

set.seed(1996+01+02)
library(data.table)
iris <- as.data.table(iris)

Setup a bootstrap function

bootstrap_func <- function(x) {

    # Resample data with replacement
    iris_boot <- iris[, .SD[sample(1:.N, size=.N, replace=TRUE)], keyby=.(Species)]

    # Summarize
    iris_boot_result <- iris_boot[, .(Sepal.Length=sum(Sepal.Length), Sepal.Width=sum(Sepal.Width)), keyby=.(Species)]
    iris_boot_result[, Ratio := Sepal.Length/Sepal.Width]
    iris_boot_result[, Sample := x]

    return(iris_boot_result[])
}

Replicate bootstrap

rep_samples <- rbindlist(lapply(1:1000, bootstrap_func), fill=TRUE)

Get results

rep_results <- rep_samples[, .(
    .N, 
    Mean=mean(Ratio), 
    Lower=quantile(Ratio,0.025), 
    Upper=quantile(Ratio, 0.975),
    Median=quantile(Ratio, 0.50),
    StdDev=sd(Ratio)
), keyby=.(Species)]
print(rep_results[])

#      Species    N     Mean    Lower    Upper   Median     StdDev
#1:     setosa 1000 1.460326 1.430836 1.489853 1.459942 0.01531591
#2: versicolor 1000 2.143124 2.089608 2.205423 2.140150 0.03010066
#3:  virginica 1000 2.216022 2.155089 2.279134 2.215410 0.03312109
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    $\begingroup$ Could you characterize "this problem" a little more specifically? What kinds of data do you anticipate applying your solution to: how many observations, what ranges of values, what amounts of correlation, what kinds of bivariate distribution, and so on? Are you asking for a closed form formula for the bootstrap CI in particular or just a formula for any reasonable CI procedure? Would approximations be acceptable? If so, how accurate must they be? $\endgroup$
    – whuber
    Dec 28 '18 at 16:46
  • 1
    $\begingroup$ Most of my datasets will be between 100K and 1M rows. Most of the values will be positive and less than 30,000. The values will definitely be correlated (probably between 0.5 and 0.8). A closed form formula would be ideal since it would run a lot faster, and I figured it probably exists? However, most estimations would suffice. I know I could probably sample down with replacement and it would be faster, but I was hoping for something better. $\endgroup$
    – Mike.Gahan
    Dec 28 '18 at 16:57
  • 2
    $\begingroup$ The distribution of the ratio of variables is typically kind of messy and might be tricky -- if the denominator variable has density > 0 at variable = 0, then the density of the ratio will have a Cauchy-like component which might cause the variance to not exist, for example. But the cdf would still be well defined, I think, except maybe in special cases (although I haven't considered this carefully). Anyway in general this problem can be approach via a change of variables approach; a web search for "change of variables probability distribution" should find some resources. $\endgroup$ Dec 28 '18 at 18:10
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    $\begingroup$ Also, long ago I worked out the distribution of the ratio of two correlated normal variables (a good fit for this problem, I think) and came up with a relatively simple formula, but later found (not surprisingly) that it had been published before. If I recall correctly it was the subject of a paper by George Marsaglia in 1967. If you can't find it, I will try to see if I have the reference somewhere. $\endgroup$ Dec 28 '18 at 18:12
  • 1
    $\begingroup$ I see that a web search for "george marsaglia ratio of correlated normal variables article" finds the original paper, an updated version, and other resources. $\endgroup$ Dec 28 '18 at 20:48
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try this one

library(rsample)
library(purrr)
library(dplyr)

iris %>% bootstraps(1000)  %>% 
   mutate(
     Ratio=map(splits,function(x){
       analysis(x) %>% 
       group_by(Species) %>% 
       summarise(Ratio=sum(Sepal.Length)/sum(Sepal.Width)) 
     })
   ) %>%
   unnest(Ratio) %>% 
   select(-id) %>% 
   group_by(Species) %>% 
   summarise( Mean = mean(Ratio),
              Lower=quantile(Ratio,0.025), 
              Upper=quantile(Ratio, 0.975),
              Median=quantile(Ratio, 0.50),
              StdDev=sd(Ratio) )
# A tibble: 3 x 6
  Species     Mean Lower Upper Median StdDev
  <fct>      <dbl> <dbl> <dbl>  <dbl>  <dbl>
1 setosa      1.46  1.43  1.49   1.46 0.0151
2 versicolor  2.14  2.08  2.20   2.14 0.0307
3 virginica   2.22  2.15  2.29   2.22 0.0333

This solution in 2.6 times faster in my tests

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    $\begingroup$ Thanks @jyjek, but this is actually quite a bit slower on larger datasets (over 100,000K rows and this thing is 5-6x slower. iris <- iris[rep(1:.N, 1000)] $\endgroup$
    – Mike.Gahan
    Dec 28 '18 at 16:53
  • $\begingroup$ perhaps using the data.table backend for dplyr: github.com/tidyverse/dtplyr $\endgroup$ Jan 18 at 21:08

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