GRIMMER-test many cases at once

Call grimmer_map() to GRIMMER-test any number of combinations of mean, standard deviation, sample size, and number of items. Mapping function for GRIMMER testing.

For summary statistics, call audit() on the results. Visualize results using grim_plot(), as with GRIM results.

Usage

grimmer_map(
  data,
  items = 1,
  merge_items = TRUE,
  x = NULL,
  sd = NULL,
  n = NULL,
  show_reason = TRUE,
  rounding = "up_or_down",
  threshold = 5,
  symmetric = FALSE,
  tolerance = .Machine$double.eps^0.5
)

Arguments

data

Data frame with columns x, sd, n, and optionally items (see documentation for grim()). Any other columns in data will be returned alongside GRIMMER test results.

items

Integer. If there is no items column in data, this specifies the number of items composing the x and sd values. Default is 1, the most common case.

merge_items

Logical. If TRUE (the default), there will be no items column in the output. Instead, values from an items column or argument will be multiplied with values in the n column. This is only for presentation and does not affect test results.

x, sd, n

Optionally, specify these arguments as column names in data.

show_reason

Logical (length 1). Should there be a reason column that shows the reasons for inconsistencies and "Passed all" for consistent values? Default is FALSE. See below for reference.

rounding, threshold, symmetric, tolerance

Further parameters of GRIMMER testing; see documentation for grimmer().

Value

A tibble with these columns –

• x, sd, n: the inputs.

• consistency: GRIMMER consistency of x, n, and items.

• reason: If consistent, "Passed all". If inconsistent, it says which test was failed (see below).

• <extra>: any columns from data other than x, n, and items.

The reason columns refers to GRIM and the three GRIMMER tests (Allard 2018). Briefly, these are:

1. The reconstructed sum of squared observations must be a whole number.

2. The reconstructed SD must match the reported one.

3. The parity of the reconstructed sum of squared observations must match the parity of the reconstructed sum of integers of which the reported means are fractions; i.e., either both are even or both are odd.

The tibble has the scr_grimmer_map class, which is recognized by the audit() generic. It also has the scr_grim_map class, so it can be visualized by grim_plot().

Summaries with audit()

There is an S3 method for audit(), so you can call audit() following grimmer_map() to get a summary of grimmer_map()'s results. It is a tibble with a single row and these columns –

1. incons_cases: number of GRIMMER-inconsistent value sets.

2. all_cases: total number of value sets.

3. incons_rate: proportion of GRIMMER-inconsistent value sets.

4. fail_grim: number of value sets that fail the GRIM test.

5. fail_test1: number of value sets that fail the first GRIMMER test (see below).

6. fail_test2: number of value sets that fail the second GRIMMER test.

7. fail_test3: number of value sets that fail the third GRIMMER test.

The reason columns refers to the three GRIMMER tests (see Allard 2018). These are:

1. The reconstructed sum of squared observations must be a whole number.

2. The reconstructed SD must match the reported one.

3. The parity of the reconstructed sum of squared observations must match the parity of the reconstructed sum of integers of which the reported means are fractions; i.e., either both are even or both are odd.

References

Allard, A. (2018). Analytic-GRIMMER: a new way of testing the possibility of standard deviations. https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/

Anaya, J. (2016). The GRIMMER test: A method for testing the validity of reported measures of variability. PeerJ Preprints. https://peerj.com/preprints/2400v1/

Examples

# Use `grimmer_map()` on data like these:
pigs5
#> # A tibble: 12 × 3
#>    x     sd        n
#>    <chr> <chr> <dbl>
#>  1 7.22  5.30     38
#>  2 4.74  6.55     31
#>  3 5.23  2.55     35
#>  4 2.57  2.57     30
#>  5 6.77  2.18     33
#>  6 2.68  2.59     34
#>  7 7.01  6.68     35
#>  8 7.38  3.65     32
#>  9 3.14  5.32     33
#> 10 6.89  4.18     37
#> 11 5.00  2.18     31
#> 12 0.24  6.43     34

# The `consistency` column shows whether
# the values to its left are GRIMMER-consistent.
# If they aren't, the `reason` column says why:
pigs5 %>%
  grimmer_map()
#> # A tibble: 12 × 5
#>    x     sd        n consistency reason                       
#>    <chr> <chr> <dbl> <lgl>       <chr>                        
#>  1 7.22  5.30     38 FALSE       GRIM inconsistent            
#>  2 4.74  6.55     31 TRUE        Passed all                   
#>  3 5.23  2.55     35 FALSE       GRIMMER inconsistent (test 3)
#>  4 2.57  2.57     30 TRUE        Passed all                   
#>  5 6.77  2.18     33 FALSE       GRIM inconsistent            
#>  6 2.68  2.59     34 TRUE        Passed all                   
#>  7 7.01  6.68     35 FALSE       GRIM inconsistent            
#>  8 7.38  3.65     32 TRUE        Passed all                   
#>  9 3.14  5.32     33 FALSE       GRIM inconsistent            
#> 10 6.89  4.18     37 TRUE        Passed all                   
#> 11 5.00  2.18     31 TRUE        Passed all                   
#> 12 0.24  6.43     34 TRUE        Passed all                   

# Get summaries with `audit()`:
pigs5 %>%
  grimmer_map() %>%
  audit()
#> # A tibble: 1 × 7
#>   incons_cases all_cases incons_rate fail_grim fail_test1 fail_test2 fail_test3
#>          <int>     <int>       <dbl>     <int>      <int>      <int>      <int>
#> 1            5        12       0.417         4          0          0          1