Treatment coding sets the mean of the reference level (base) as the intercept. Each contrast compares one level with the reference level.
contr_code_treatment(fct, levels = NULL, base = 1)the factor to contrast code (or a vector)
the levels of the factor in order
the index of the level to use as baseline
the factor with contrasts set
df <- sim_design(between = list(pet = c("cat", "dog")),
mu = c(10, 20), plot = FALSE)
df$pet <- contr_code_treatment(df$pet)
lm(y ~ pet, df) %>% broom::tidy()
#> # A tibble: 2 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 10.0 0.0955 105. 1.07e-175
#> 2 pet.dog-cat 9.94 0.135 73.6 8.78e-146
df <- sim_design(between = list(pet = c("cat", "dog", "ferret")),
mu = c(2, 4, 9), empirical = TRUE, plot = FALSE)
df$pet <- contr_code_treatment(df$pet)
lm(y ~ pet, df) %>% broom::tidy()
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 2.00 0.100 20.0 5.87e- 57
#> 2 pet.dog-cat 2.00 0.141 14.1 4.53e- 35
#> 3 pet.ferret-cat 7.00 0.141 49.5 1.67e-145
df$pet <- contr_code_treatment(df$pet, base = 2)
lm(y ~ pet, df) %>% broom::tidy()
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 4.00 0.100 40 1.30e-121
#> 2 pet.cat-dog -2.00 0.141 -14.1 4.53e- 35
#> 3 pet.ferret-dog 5.00 0.141 35.4 1.89e-108
df$pet <- contr_code_treatment(df$pet, base = "ferret")
lm(y ~ pet, df) %>% broom::tidy()
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 9.00 0.100 90.0 1.40e-217
#> 2 pet.cat-ferret -7.00 0.141 -49.5 1.67e-145
#> 3 pet.dog-ferret -5 0.141 -35.4 1.89e-108