GRIM-testing with dispersed inputs

grim_map_seq() performs GRIM-testing with values surrounding the input values. This provides an easy and powerful way to assess whether small errors in computing or reporting may be responsible for GRIM inconsistencies in published statistics.

Call audit_seq() on the results for summary statistics.

Usage

grim_map_seq(
  data,
  x = NULL,
  n = NULL,
  var = Inf,
  dispersion = 1:5,
  out_min = "auto",
  out_max = NULL,
  include_reported = FALSE,
  include_consistent = FALSE,
  ...
)

Arguments

data: A data frame that grim_map() could take.
x, n: Optionally, specify these arguments as column names in data.
var: String. Names of the columns that will be dispersed. Default is c("x", "n").
dispersion: Numeric. Sequence with steps up and down from the var inputs. It will be adjusted to these values' decimal levels. For example, with a reported 8.34, the step size is 0.01. Default is 1:5, for five steps up and down.
out_min, out_max: If specified, output will be restricted so that it's not below out_min or above out_max. Defaults are "auto" for out_min, i.e., a minimum of one decimal unit above zero; and NULL for out_max, i.e., no maximum.
include_reported: Logical. Should the reported values themselves be included in the sequences originating from them? Default is FALSE because this might be redundant and bias the results.
include_consistent: Logical. Should the function also process consistent cases (from among those reported), not just inconsistent ones? Default is FALSE because the focus should be on clarifying inconsistencies.
...: Arguments passed down to grim_map().

Value

A tibble (data frame) with detailed test results.

Summaries with `audit_seq()`

You can call audit_seq() following grim_map_seq(). It will return a data frame with these columns:

x and n are the original inputs, tested for consistency here.
hits_total is the total number of GRIM-consistent value sets found within the specified dispersion range.
hits_x is the number of GRIM-consistent value sets found by varying x.
Accordingly with n and hits_n.
(Note that any consistent reported cases will be counted by the hits_* columns if both include_reported and include_consistent are set to TRUE.)
diff_x reports the absolute difference between x and the next consistent dispersed value (in dispersion steps, not the actual numeric difference). diff_x_up and diff_x_down report the difference to the next higher or lower consistent value, respectively.
diff_n, diff_n_up, and diff_n_down do the same for n.

Call audit() following audit_seq() to summarize results even further. It's mostly self-explaining, but na_count and na_rate are the number and rate of times that a difference could not be computed because of a lack of corresponding hits within the dispersion range.

Examples

# `grim_map_seq()` can take any input
# that `grim_map()` can take:
pigs1
#> # A tibble: 12 × 2
#>    x         n
#>    <chr> <dbl>
#>  1 7.22     32
#>  2 4.74     25
#>  3 5.23     29
#>  4 2.57     24
#>  5 6.77     27
#>  6 2.68     28
#>  7 7.01     29
#>  8 7.38     26
#>  9 3.14     27
#> 10 6.89     31
#> 11 5.00     25
#> 12 0.24     28

# All the results:
out <- grim_map_seq(pigs1, include_consistent = TRUE)
out
#> # A tibble: 240 × 7
#>    x         n consistency probability diff_var  case var  
#>    <chr> <int> <lgl>             <dbl>    <int> <int> <chr>
#>  1 7.17     32 FALSE              0.68       -5     1 x    
#>  2 7.18     32 FALSE              0.68       -4     1 x    
#>  3 7.19     32 TRUE               0.68       -3     1 x    
#>  4 7.20     32 FALSE              0.68       -2     1 x    
#>  5 7.21     32 FALSE              0.68       -1     1 x    
#>  6 7.23     32 FALSE              0.68        1     1 x    
#>  7 7.24     32 FALSE              0.68        2     1 x    
#>  8 7.25     32 TRUE               0.68        3     1 x    
#>  9 7.26     32 FALSE              0.68        4     1 x    
#> 10 7.27     32 FALSE              0.68        5     1 x    
#> # ℹ 230 more rows

# Case-wise summaries with `audit_seq()`
# can be more important than the raw results:
out %>%
  audit_seq()
#> # A tibble: 12 × 12
#>    x         n consistency hits_total hits_x hits_n diff_x diff_x_up diff_x_down
#>    <chr> <int> <lgl>            <int>  <int>  <int>  <int>     <int>       <int>
#>  1 7.22     32 TRUE                 5      2      3      3         3          -3
#>  2 4.74     25 FALSE                4      2      2      2         2          -2
#>  3 5.23     29 FALSE                6      3      3      1         1          -2
#>  4 2.57     24 FALSE                6      3      3      1         1          -3
#>  5 6.77     27 FALSE                7      3      4      1         1          -3
#>  6 2.68     28 TRUE                 4      2      2      3         3          -4
#>  7 7.01     29 FALSE                3      3      0      1         2          -1
#>  8 7.38     26 TRUE                 5      2      3      3         4          -3
#>  9 3.14     27 FALSE                6      3      3      1         1          -3
#> 10 6.89     31 FALSE                8      4      4      1         1          -2
#> 11 5.00     25 TRUE                12      2     10      4         4          -4
#> 12 0.24     28 FALSE                6      3      3      1         1          -3
#> # ℹ 3 more variables: diff_n <int>, diff_n_up <int>, diff_n_down <int>

Usage

Arguments

Value

Summaries with audit_seq()

Examples

Summaries with `audit_seq()`