Appropriate to use t_to_d() on estimated marginal mean contrasts from ANOVA? #616

library(palmerpenguins) # dataset
#> Warning: package 'palmerpenguins' was built under R version 4.1.3
library(car) # anova functions
#> Warning: package 'car' was built under R version 4.1.3
#> Loading required package: carData
#> Warning: package 'carData' was built under R version 4.1.3
library(effectsize) # effect sizes
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.1.3
#> 
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:car':
#> 
#>     recode
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

# assigns data to a dataframe we call "df"
df <- palmerpenguins::penguins

# drop rows with missing values
df <- df[complete.cases(df)==TRUE, ]


df %>%
    group_by(species, sex) %>% # Group by the specified variables
    dplyr::summarise(n()) %>%
  knitr::kable()
#> `summarise()` has grouped output by 'species'. You can override using the
#> `.groups` argument.

# Fit data
flipper_fit <- stats::aov(flipper_length_mm ~ species, data = df)
# Run anova
flipper_anova <- car::Anova(flipper_fit)


# Extract estimated marginal means
flipper_emmeans <- emmeans::emmeans(flipper_fit, specs = pairwise ~ species)

# convert estimated marginal mean contrasts to dataframe
flipper_emmeans_contrasts <- data.frame(flipper_emmeans$contrasts)

knitr::kable(flipper_emmeans_contrasts)

effectsize::t_to_d(t = flipper_emmeans_contrasts[flipper_emmeans_contrasts$contrast == "Adelie - Chinstrap", "t.ratio"],
                   df_error = flipper_emmeans_contrasts[flipper_emmeans_contrasts$contrast == "Adelie - Chinstrap", "df"]
                   )
#> d     |         95% CI
#> ----------------------
#> -0.64 | [-0.86, -0.42]