comparisons vs contrasts

gistfile1.R

library(easystats)
#> # Attaching packages: easystats 0.4.3
#> ✔ insight     0.17.0.6   ✔ datawizard  0.4.0.14
#> ✔ bayestestR  0.12.1     ✔ performance 0.9.0.3 
#> ✔ parameters  0.17.0.9   ✔ effectsize  0.6.0.7 
#> ✔ modelbased  0.8.0      ✔ correlation 0.8.0.1 
#> ✔ see         0.7.0.2    ✔ report      0.5.1.1
library(emmeans)
library(marginaleffects)

data(efc, package = "datawizard")
efc <- data_to_factor(efc, select = c("c172code", "e16sex"), append = TRUE)
efc$c172code_f <- data_recode(efc$c172code_f, list(`low` = 1:2, `high` = 3))
m <- lm(neg_c_7 ~ c172code_f * e16sex_f, data = efc)

emmeans(m, c("c172code_f", "e16sex_f")) |> 
  contrast(method = "pairwise") |> 
  model_parameters()
#> contrast        | Coefficient |   SE |        95% CI | t(83) |      p
#> ---------------------------------------------------------------------
#> high 1 - low 1  |        0.53 | 2.27 | [-3.99, 5.04] |  0.23 | > .999
#> high 1 - high 2 |        0.85 | 2.55 | [-4.22, 5.92] |  0.33 | > .999
#> high 1 - low 2  |        2.83 | 2.27 | [-1.68, 7.34] |  1.25 | 0.811 
#> low 1 - high 2  |        0.32 | 1.54 | [-2.74, 3.38] |  0.21 | > .999
#> low 1 - low 2   |        2.30 | 1.01 | [ 0.30, 4.31] |  2.29 | 0.212 
#> high 2 - low 2  |        1.98 | 1.53 | [-1.07, 5.03] |  1.29 | 0.789 
#> 
#> p-value adjustment method: Tukey

comparisons(
  m, 
  c("c172code_f", "e16sex_f"), 
  newdata = get_datagrid(m, at = c("c172code_f", "e16sex_f")),
  # same as: datagrid(model = m, c172code_f = c("low", "high"), e16sex_f = c(1, 2)),
  contrast_factor = "pairwise"
)
#>    rowid       term   contrast comparison std.error c172code_f e16sex_f
#> 1:     1 c172code_f low - high -0.5277778  2.269220       high        1
#> 2:     2 c172code_f low - high -0.5277778  2.269220        low        1
#> 3:     3 c172code_f low - high -1.9810811  1.534532       high        2
#> 4:     4 c172code_f low - high -1.9810811  1.534532        low        2
#> 5:     1   e16sex_f      2 - 1 -0.8500000  2.547193       high        1
#> 6:     2   e16sex_f      2 - 1 -2.3033033  1.007946        low        1
#> 7:     3   e16sex_f      2 - 1 -0.8500000  2.547193       high        2
#> 8:     4   e16sex_f      2 - 1 -2.3033033  1.007946        low        2