grimmer_map_total_n {scrutiny}R Documentation

GRIMMER-testing with hypothetical group sizes

Description

When reporting group means, some published studies only report the total sample size but no group sizes corresponding to each mean. However, group sizes are crucial for GRIMMER-testing.

In the two-groups case, grimmer_map_total_n() helps in these ways:

All of this works with one or more total sample sizes at a time. Call audit_total_n() for summary statistics.

Usage

grimmer_map_total_n(
  data,
  x1 = NULL,
  x2 = NULL,
  sd1 = NULL,
  sd2 = NULL,
  dispersion = 0:5,
  n_min = 1L,
  n_max = NULL,
  constant = NULL,
  constant_index = NULL,
  ...
)

Arguments

data

Data frame with string columns x1, x2, sd1, and sd2, as well as numeric column n. The first two are reported group means. sd1 and sd2 are reported group SDs. n is the reported total sample size. It is not very important whether a value is in x1 or in x2 because, after the first round of tests, the function switches roles between x1 and x2, and reports the outcomes both ways. The same applies to sd1 and sd2. However, do make sure the ⁠x*⁠ and ⁠sd*⁠ values are paired accurately, as reported.

x1, x2, sd1, sd2

Optionally, specify these arguments as column names in data.

dispersion

Numeric. Steps up and down from half the n values. Default is 0:5, i.e., half n itself followed by five steps up and down.

n_min

Numeric. Minimal group size. Default is 1.

n_max

Numeric. Maximal group size. Default is NULL, i.e., no maximum.

constant

Optionally, add a length-2 vector or a list of length-2 vectors (such as a data frame with exactly two rows) to accompany the pairs of dispersed values. Default is NULL, i.e., no constant values.

constant_index

Integer (length 1). Index of constant or the first constant column in the output tibble. If NULL (the default), constant will go to the right of n_change.

...

Arguments passed down to grimmer_map(). (NOTE: Don't use the items argument. It currently contains a bug that will be fixed in the future.)

Value

A tibble with these columns:

Summaries with audit_total_n()

You can call audit_total_n() following up on grimmer_map_total_n() to get a tibble with summary statistics. It will have these columns:

References

Bauer, P. J., & Francis, G. (2021). Expression of Concern: Is It Light or Dark? Recalling Moral Behavior Changes Perception of Brightness. Psychological Science, 32(12), 2042–2043. https://journals.sagepub.com/doi/10.1177/09567976211058727

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/

Bauer, P. J., & Francis, G. (2021). Expression of Concern: Is It Light or Dark? Recalling Moral Behavior Changes Perception of Brightness. Psychological Science, 32(12), 2042–2043. https://journals.sagepub.com/doi/10.1177/09567976211058727

See Also

function_map_total_n(), which created the present function using grimmer_map().

Examples

# Run `grimmer_map_total_n()` on data like these:
df <- tibble::tribble(
  ~x1,    ~x2,    ~sd1,   ~sd2,   ~n,
  "3.43", "5.28", "1.09", "2.12", 70,
  "2.97", "4.42", "0.43", "1.65", 65
)
df

grimmer_map_total_n(df)

# `audit_total_n()` summaries can be more important than
# the detailed results themselves.
# The `hits_total` column shows all scenarios in
# which both divergent `n` values are GRIMMER-consistent
# with the `x*` values when paired with them both ways:
df %>%
  grimmer_map_total_n() %>%
  audit_total_n()

# By default (`dispersion = 0:5`), the function goes
# five steps up and down from `n`. If this sequence
# gets longer, the number of hits tends to increase:
df %>%
  grimmer_map_total_n(dispersion = 0:10) %>%
  audit_total_n()

[Package scrutiny version 0.4.0 Index]