计算数据帧内的置信区间（二项式）

Question

I want to get the confidence intervals for proportions within my tibble. Is there a way of doing this?

library(tidyverse)
library(Hmisc)
library(broom)


df <- tibble(id = c(1, 2, 3, 4, 5, 6),
  count = c(4, 1, 22, 4545, 33, 23),
           n = c(22, 65, 34, 6323, 35, 45))

Which looks like this:

# A tibble: 6 x 3
     id count     n
  <dbl> <dbl> <dbl>
1     1     4    22
2     2     1    65
3     3    22    34
4     4  4545  6323
5     5    33    35
6     6    23    45

Using binconf from Hmisc and tidy from broom the solution could be from any package:

The intervals for the first row:

tidy(binconf(4, 22))

# A tibble: 1 x 4
  .rownames PointEst  Lower Upper
  <chr>        <dbl>  <dbl> <dbl>
1 ""           0.182 0.0731 0.385

I have tried using map in purrr but get errors:

map(df, tidy(binconf(count, n)))

Error in x[i] : object of type 'closure' is not subsettable

I could just calculate them using dplyr but I get values below zero (eg row 2) or above one (eg row 5), which I don't want. eg

df %>% 
  mutate(prop = count / n) %>%
  mutate(se = (sqrt(prop * (1-prop)/n))) %>% 
  mutate(lower = prop - (se*1.96)) %>% 
  mutate(upper = prop + (se*1.96))

# A tibble: 6 x 7
     id count     n   prop      se   lower  upper
  <dbl> <dbl> <dbl>  <dbl>   <dbl>   <dbl>  <dbl>
1     1     4    22 0.182  0.0822   0.0206 0.343 
2     2     1    65 0.0154 0.0153  -0.0145 0.0453
3     3    22    34 0.647  0.0820   0.486  0.808 
4     4  4545  6323 0.719  0.00565  0.708  0.730 
5     5    33    35 0.943  0.0392   0.866  1.02  
6     6    23    45 0.511  0.0745   0.365  0.657 

Is there a good way of doing this? I did have a look at the confint_tidy() function, but could not get that to work. Any ideas?

Answer 1

It may not be tidy but

> as.tibble(cbind(df, binconf(df$count, df$n)))
# A tibble: 6 x 6
     id count     n PointEst    Lower  Upper
  <dbl> <dbl> <dbl>    <dbl>    <dbl>  <dbl>
1     1     4    22   0.182  0.0731   0.385 
2     2     1    65   0.0154 0.000789 0.0821
3     3    22    34   0.647  0.479    0.785 
4     4  4545  6323   0.719  0.708    0.730 
5     5    33    35   0.943  0.814    0.984 
6     6    23    45   0.511  0.370    0.650 

seems to work