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 = deprecated(),
  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 is only for presentation and does not affect test results.
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. No longer supported: now, there is always a probability column. (It replaces the earlier ratio column.)
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.
probability: the probability of GRIM inconsistency; see grim_probability().
<extra>: any columns from data other than x, n, and items.

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_prob: average probability of GRIM inconsistency.
incons_to_prob: ratio of incons_rate to mean_grim_prob.
testable_cases: number of GRIM-testable value sets (i.e., those with a positive probability).
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 probability
#>    <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 probability rec_sum rec_x_upper rec_x_lower
#>    <chr> <dbl> <lgl>             <dbl>   <dbl>       <dbl>       <dbl>
#>  1 7.22     32 TRUE               0.68  231.         7.25        7.22 
#>  2 4.74     25 FALSE              0.75  118.         4.76        4.72 
#>  3 5.23     29 FALSE              0.71  152.         5.24        5.21 
#>  4 2.57     24 FALSE              0.76   61.7        2.58        2.54 
#>  5 6.77     27 FALSE              0.73  183.         6.78        6.74 
#>  6 2.68     28 TRUE               0.72   75.0        2.71        2.68 
#>  7 7.01     29 FALSE              0.71  203.         7.03        7.00 
#>  8 7.38     26 TRUE               0.74  192.         7.38        7.35 
#>  9 3.14     27 FALSE              0.73   84.8        3.15        3.11 
#> 10 6.89     31 FALSE              0.69  214.         6.90        6.87 
#> 11 5.00     25 TRUE               0.75  125          5.00        5.00 
#> 12 0.24     28 FALSE              0.72    6.72       0.250       0.214
#> # ℹ 4 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>

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