summary.pop_data.md
In serocalculator: Estimating Infection Rates from Serological Data

`summary.pop_data()` produces stable results when `strata = NULL`

Code
  xs_data %>% summary(strata = NULL)
Output

  n = 200

  Distribution of age:

  # A tibble: 1 x 7
        n   min first_quartile median  mean third_quartile   max
    <int> <dbl>          <dbl>  <dbl> <dbl>          <dbl> <dbl>
  1   200   2.3           6.75     12  11.7             16    24

  Distributions of antigen-isotype measurements:

  # A tibble: 2 x 7
    antigen_iso   Min `1st Qu.` Median `3rd Qu.`   Max `# NAs`
    <fct>       <dbl>     <dbl>  <dbl>     <dbl> <dbl>   <int>
  1 HlyE_IgA    0          1.34   2.64      4.15  69.9       0
  2 HlyE_IgG    0.217      1.36   2.72      6.95  64.5       0

`summary.pop_data()` produces stable results with stratification

Code
  xs_data %>% summary(strata = "catchment")
Output

  n = 200

  Distribution of age:

  # A tibble: 2 x 8
    catchment     n   min first_quartile median  mean third_quartile   max
    <chr>     <int> <dbl>          <dbl>  <dbl> <dbl>          <dbl> <dbl>
  1 kgh          94   2.3           6.85     11  11.6           15.9    24
  2 aku         106   2.3           6.6      12  11.8           16      23

  Distributions of antigen-isotype measurements:

  # A tibble: 4 x 8
    antigen_iso catchment    Min `1st Qu.` Median `3rd Qu.`   Max `# NAs`
    <fct>       <chr>      <dbl>     <dbl>  <dbl>     <dbl> <dbl>   <int>
  1 HlyE_IgA    kgh       0          1.55    2.90      3.99  45.6       0
  2 HlyE_IgA    aku       0.0308     1.20    2.00      5.74  69.9       0
  3 HlyE_IgG    kgh       0.362      1.99    2.79      8.06  64.5       0
  4 HlyE_IgG    aku       0.217      0.983   2.12      5.78  33.6       0