GRIM-test many cases at once

Call grim_map() to GRIM-test any number of combinations of mean/proportion, sample size, and number of items. Mapping function for GRIM-testing.

Set percent to TRUE if the x values are percentages. This will convert x values to decimals and adjust the decimal count accordingly.

Display intermediary numbers from GRIM-testing in columns by setting show_rec to TRUE.

For summary statistics, call [audit()] on the results.

Usage

grim_map(
  data,
  items = 1,
  merge_items = TRUE,
  percent = FALSE,
  x = NULL,
  n = NULL,
  show_rec = FALSE,
  show_prob = FALSE,
  rounding = "up_or_down",
  threshold = 5,
  symmetric = FALSE,
  tolerance = .Machine$double.eps^0.5,
  testables_only = FALSE,
  extra = Inf
)

Arguments

data: Data frame with columns x, n, and optionally items (see documentation for grim(). By default, any other columns in data will be returned alongside GRIM test results (see extra below).
items: Integer. If there is no items column in data, this specifies the number of items composing the x 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 does not affect GRIM-testing.
percent: Logical. Set percent to TRUE if the x values are percentages. This will convert them to decimal numbers and adjust the decimal count (i.e., increase it by 2). It also affects the ratio column. Default is FALSE.
x, n: Optionally, specify these arguments as column names in data.
show_rec: Logical. If set to TRUE, the reconstructed numbers from GRIM-testing are shown as columns. See section Reconstructed numbers below. Default is FALSE.
show_prob: Logical. If set to TRUE, adds a prob column that contains the probability of GRIM inconsistency. This is simply the ratio column censored to range between 0 and 1. Default is FALSE.
rounding, threshold, symmetric, tolerance: Further parameters of GRIM-testing; see documentation for grim().
testables_only: Logical. If testables_only is set to TRUE, only GRIM-testable cases (i.e., those with a positive GRIM ratio) are included. Default is FALSE.
extra: String or integer. The other column(s) from data to be returned in the output tibble alongside test results, referenced by their name(s) or number(s). Default is Inf, which returns all columns. To return none of them, set extra to 0.

Value

A tibble with these columns --

x, n: the inputs.
consistency: GRIM consistency of x, n, and items.
<extra>: any columns from data other than x, n, and items.
ratio: the GRIM ratio; see grim_ratio().

The tibble has the scr_grim_map class, which is recognized by the audit() generic.

Reconstructed numbers

If show_rec is set to TRUE, the output includes the following additional columns:

rec_sum: the sum total from which the mean or proportion was ostensibly derived.
rec_x_upper: the upper reconstructed x value.
rec_x_lower: the lower reconstructed x value.
rec_x_upper_rounded: the rounded rec_x_upper value.
rec_x_lower_rounded: the rounded rec_x_lower value.

With the default for rounding, "up_or_down", each of the last two columns is replaced by two columns that specify the rounding procedures (i.e., "_up" and "_down").

Summaries with `audit()`

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

incons_cases: number of GRIM-inconsistent value sets.
all_cases: total number of value sets.
incons_rate: proportion of GRIM-inconsistent value sets.
mean_grim_ratio: average of GRIM ratios.
incons_to_ratio: ratio of incons_rate to mean_grim_ratio.
testable_cases: number of GRIM-testable value sets (i.e., those with a positive ratio).
testable_rate: proportion of GRIM-testable value sets.

References

Brown, N. J. L., & Heathers, J. A. J. (2017). The GRIM Test: A Simple Technique Detects Numerous Anomalies in the Reporting of Results in Psychology. Social Psychological and Personality Science, 8(4), 363–369. https://journals.sagepub.com/doi/10.1177/1948550616673876

Examples

# Use `grim_map()` on data like these:
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

# The `consistency` column shows
# whether the values to its left
# are GRIM-consistent:
pigs1 %>%
  grim_map()
#> # A tibble: 12 × 4
#>    x         n consistency ratio
#>    <chr> <dbl> <lgl>       <dbl>
#>  1 7.22     32 TRUE         0.68
#>  2 4.74     25 FALSE        0.75
#>  3 5.23     29 FALSE        0.71
#>  4 2.57     24 FALSE        0.76
#>  5 6.77     27 FALSE        0.73
#>  6 2.68     28 TRUE         0.72
#>  7 7.01     29 FALSE        0.71
#>  8 7.38     26 TRUE         0.74
#>  9 3.14     27 FALSE        0.73
#> 10 6.89     31 FALSE        0.69
#> 11 5.00     25 TRUE         0.75
#> 12 0.24     28 FALSE        0.72

# Display intermediary numbers from
# GRIM-testing with `show_rec = TRUE`:
pigs1 %>%
  grim_map(show_rec = TRUE)
#> # A tibble: 12 × 11
#>    x         n consistency rec_sum rec_x_upper rec_x_lower
#>    <chr> <dbl> <lgl>         <dbl>       <dbl>       <dbl>
#>  1 7.22     32 TRUE         231.         7.25        7.22 
#>  2 4.74     25 FALSE        118.         4.76        4.72 
#>  3 5.23     29 FALSE        152.         5.24        5.21 
#>  4 2.57     24 FALSE         61.7        2.58        2.54 
#>  5 6.77     27 FALSE        183.         6.78        6.74 
#>  6 2.68     28 TRUE          75.0        2.71        2.68 
#>  7 7.01     29 FALSE        203.         7.03        7.00 
#>  8 7.38     26 TRUE         192.         7.38        7.35 
#>  9 3.14     27 FALSE         84.8        3.15        3.11 
#> 10 6.89     31 FALSE        214.         6.90        6.87 
#> 11 5.00     25 TRUE         125          5.00        5.00 
#> 12 0.24     28 FALSE          6.72       0.250       0.214
#> # ℹ 5 more variables: rec_x_upper_rounded_up <dbl>,
#> #   rec_x_upper_rounded_down <dbl>, rec_x_lower_rounded_up <dbl>,
#> #   rec_x_lower_rounded_down <dbl>, ratio <dbl>

# Get summaries with `audit()`:
pigs1 %>%
  grim_map() %>%
  audit()
#> # A tibble: 1 × 7
#>   incons_cases all_cases incons_rate mean_grim_ratio incons_to_ratio
#>          <int>     <int>       <dbl>           <dbl>           <dbl>
#> 1            8        12       0.667           0.724           0.921
#> # ℹ 2 more variables: testable_cases <int>, testable_rate <dbl>