Tukey-Kramer test — Tukey_Kramer

Represent significance statements resulting from all-pairwise comparisons.

Usage

Tukey_Kramer_test(
  data,
  formula = NULL,
  alpha = 0.05,
  rounding = 4,
  verbose = TRUE
)

Arguments

data: A data frame in which the variables specified in the formula will be found.
formula: A formula specifying the model.
alpha: Numeric value range from 0 to 1 (default: 0.05). The error tolerance.
rounding: Integer (default: 4). Rounding digits.
verbose: Logical (default: TRUE). Show warnings and messages.

Value

A list with 4 elements:

method: Statistical procedures that were conducted.
data: The input data and possibly other transformed data.
pre_hoc: a priori test result.
post_hoc: Post-hoc test result.
summary: Descriptive statistics.

References

Howell, D. C. (2013). Statistical Methods for Psychology (8th ed.). Cengage Learning. Chapter 12: Multiple comparisons among treatment means, Section 12.7: Tukey's test, pg. 394.

Zar, J. H. (2014). Biostatistical analysis (5th edition). Pearson. Chapter 11: Multiple comparisons, pg. 244-245.

Examples

out <- Tukey_Kramer_test(O_O_X, val ~ grp)
#> 
#> -------------------------------------------
#> Tukey-Kramer multiple comparison procedure
#> -------------------------------------------
#> Data: O_O_X ; Formula: val ~ grp
#> 
#>   GROUP CLD  N    AVG     SD    MED
#> 1    G1   a 27 5.9377 0.9802 5.7820
#> 2    G2   a 24 6.1523 0.8466 6.0457
#> 3    G3   c 16 3.0106 0.8171 3.0477
#> 4    G4   b 30 5.2209 0.9513 5.2100
#> 5    G5   d 11 1.6157 0.8302 1.6525
#> 6    G6   c 20 3.8619 0.7809 3.8442
#>