grimmer_map {scrutiny}

GRIMMER-test many cases at once

Description

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

(NOTE: Don't use the items argument. It currently contains a bug that will be fixed in the future.) 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 does not affect GRIM- or GRIMMER-testing.

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 NA for consistent values)? Default is FALSE.

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.

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

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 (sum of squares is a whole number).

6. fail_test2: number of value sets that fail the second GRIMMER test (matching SDs).

7. fail_test3: number of value sets that fail the third GRIMMER test (equal parity).

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

# 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()

# Get summaries with `audit()`:
pigs5 %>%
  grimmer_map() %>%
  audit()