Convert emmeans contrast methods into comparison functions suitable for use with compare_levels().

emmeans_comparison(method, ...)

Arguments

method

An emmeans-style contrast method. One of: (1) a string specifying the name of an emmeans contrast method, like "pairwise", "trt.vs.ctrl", "eff"; or (2) an emmeans-style contrast function itself, like emmeans::pairwise.emmc, emmeans::trt.vs.ctrl.emmc, etc, or a custom function that takes a vector of factor levels and returns a contrast matrix.

...

Arguments passed on to the contrast method.

Value

A function that takes a single argument, var, containing a variable to generate contrasts for (e.g., a factor or a character vector) and returns a function that generates a list of named unevaluated expressions representing different contrasts of that variable. This function is suitable to be used as the comparison argument in compare_levels().

Details

Given an emmeans contrast method name as a string (e.g., "pairwise", "trt.vs.ctrl", etc) or an emmeans-style contrast function (e.g., emmeans::pairwise.emmc, emmeans::trt.vs.ctrl.emmc, etc), emmeans_comparison() returns a new function that can be used in the comparison argument to compare_levels() to compute those contrasts.

See also

compare_levels(), emmeans::contrast-methods. See gather_emmeans_draws() for a different approach to using emmeans with tidybayes.

Author

Matthew Kay

Examples


library(dplyr)
library(ggplot2)

data(RankCorr, package = "ggdist")

# emmeans contrast methods return matrices. E.g. the "eff" comparison
# compares each level to the average of all levels:
emmeans:::eff.emmc(c("a","b","c","d"))
#>   a effect b effect c effect d effect
#> a     0.75    -0.25    -0.25    -0.25
#> b    -0.25     0.75    -0.25    -0.25
#> c    -0.25    -0.25     0.75    -0.25
#> d    -0.25    -0.25    -0.25     0.75

# tidybayes::compare_levels() can't use a contrast matrix like this
# directly; it takes arbitrary expressions of factor levels. But
# we can use `emmeans_comparison` to generate the equivalent expressions:
emmeans_comparison("eff")(c("a","b","c","d"))
#> $`a effect`
#> 0.75 * a + -0.25 * b + -0.25 * c + -0.25 * d
#> 
#> $`b effect`
#> -0.25 * a + 0.75 * b + -0.25 * c + -0.25 * d
#> 
#> $`c effect`
#> -0.25 * a + -0.25 * b + 0.75 * c + -0.25 * d
#> 
#> $`d effect`
#> -0.25 * a + -0.25 * b + -0.25 * c + 0.75 * d
#> 

# We can use the "eff" comparison type with `compare_levels()` as follows:
RankCorr %>%
  spread_draws(b[i,j]) %>%
  filter(j == 1) %>%
  compare_levels(b, by = i, comparison = emmeans_comparison("eff")) %>%
  median_qi()
#> # A tibble: 3 x 8
#>   i            j       b .lower .upper .width .point .interval
#>   <chr>    <int>   <dbl>  <dbl>  <dbl>  <dbl> <chr>  <chr>    
#> 1 1 effect     1 -0.361  -0.546 -0.168   0.95 median qi       
#> 2 2 effect     1 -0.0209 -0.243  0.208   0.95 median qi       
#> 3 3 effect     1  0.376   0.159  0.598   0.95 median qi