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A wrapper for anovakun (version 4.8.9) with improved usability and formatting

Source: R/anovakun_tidy.r
anovakun_tidy.Rd

This function serves as a wrapper around anovakun_ (version 4.8.9), streamlining common usage patterns in mutolab and formatting the results for easier interpretation.

Usage

anovakun_tidy(
  dataset,
  design,
  alpha = 0.05,
  inc_allvar = FALSE,
  do_round = TRUE,
  ...
)

Arguments

dataset

A data frame containing the input data in long (tidy) format.

design

A character string specifying the experimental design (e.g., "As", "ABs", "sA", "sAB", "AsB", etc.).

alpha

Significance level. Defaults to 0.05.

inc_allvar

Logical. If TRUE, all columns will be included in the output. Defaults to FALSE.

do_round

Logical. If TRUE, numeric values in the output will be rounded for readability. Defaults to TRUE.

...

Additional arguments passed to anovakun_. The number of levels for each factor is automatically calculated from dataset and design, and does not need to be specified manually.

Value

A data frame containing the results of ANOVA, including F-statistics, p-values, effect sizes, and sphericity indices (if applicable).

Details

While the core computation is delegated to the original anovakun function, this wrapper automatically infers the number of factor levels, simplifies output, and adds formatting options such as rounding.

For full details of the original implementation of anovakun, refer to: https://riseki.cloudfree.jp/?ANOVA%E5%90%9B

See also

anovakun_ for the original function wrapped by this helper.

Examples

data_snakemr %>%
  anovakun_tidy("sABC")
#> A 2*2*5 within-participants ANOVA was performed.
#>             effect  df1   df2      F     p sig  eta2 peta2 cohens_f epsilon_CM
#> 1            shape 1.00 23.00  37.31 0.000   * 0.019 0.619    1.274      1.000
#> 2             face 1.00 23.00  98.23 0.000   * 0.025 0.810    2.067      1.000
#> 3            angle 1.60 36.83 201.16 0.000   * 0.458 0.897    2.957      0.400
#> 4       shape:face 1.00 23.00   9.72 0.005   * 0.003 0.297    0.650      1.000
#> 5      shape:angle 3.45 79.26   6.91 0.000   * 0.003 0.231    0.548      0.861
#> 6       face:angle 4.00 92.00   3.87 0.006   * 0.002 0.144    0.410      1.000
#> 7 shape:face:angle 4.00 92.00   1.37 0.249  ns 0.001 0.056    0.244      1.000