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This package provides utilities to set different common contrast
coding schemes for use with regression models. Detailed usage is
available in the contrasts vignette with
vignette('contrasts', 'contrastable').
You can install from CRAN with:
install.packages("contrastable")You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("tsostarics/contrastable", build_vignettes = TRUE)To cite contrastable in publications, please use
Sostarics, T. (2024). contrastable: Contrast Coding Utilities in R. R package version 1.0.1.
A BibTeX entry for LaTeX users is
@Manual{,
author = {Thomas Sostarics},
title = {{contrastable}: Contrast Coding Utilities in {R}},
year = {2024},
note = {R package version 1.0.1},
url = {https://github.com/tsostarics/contrastable},
doi = {10.5281/zenodo.11869427},
}See the Citation Examples section in the contrasts vignette for suggestions and examples of how to cite this package in a paper.
Here is a simple example showing how to set particular factors to a specific contrast scheme.
library(contrastable)
my_data <- mtcars
my_data$gear <- ordered(my_data$gear) # Set as ordered factor in dataframeset_contrasts can be used to set the contrasts onto the
dataframe itself, which is needed when a modeling function lacks a
contrasts argument.
# Specify the contrast schemes we want, factor conversion done automatically
# Set reference level with + and intercept with *
my_data <- set_contrasts(my_data,
cyl ~ scaled_sum_code + 6,
carb ~ helmert_code,
vs ~ treatment_code + 1,
print_contrasts = TRUE)
#> Converting to factors: cyl carb vs
#> Expect contr.treatment or contr.poly for unset factors: gear
#> $cyl
#> 4 8
#> 4 2/3 -1/3
#> 6 -1/3 -1/3
#> 8 -1/3 2/3
#>
#> $carb
#> <2 <3 <4 <6 <8
#> 1 -1/2 -1/3 -1/4 -1/5 -1/6
#> 2 1/2 -1/3 -1/4 -1/5 -1/6
#> 3 0 2/3 -1/4 -1/5 -1/6
#> 4 0 0 3/4 -1/5 -1/6
#> 6 0 0 0 4/5 -1/6
#> 8 0 0 0 0 5/6
#>
#> $vs
#> 0
#> 0 1
#> 1 0We can use glimpse_contrasts to get information about
the factors and diagnostics about the scheme we have set.
# Create a reusable list to use with multiple functions
contrast_schemes <- list(
cyl ~ scaled_sum_code + 6,
carb ~ helmert_code,
vs ~ treatment_code + 1
)
# Get information about our contrasts, even those we didn't explicitly set
# (gear is ordered, and so uses contr.poly by default)
glimpse_contrasts(my_data,
contrast_schemes,
add_namespace = TRUE,
show_all_factors = TRUE) |>
knitr::kable()| factor | n | level_names | scheme | reference | intercept |
|---|---|---|---|---|---|
| cyl | 3 | 4, 6, 8 | contrastable::scaled_sum_code | 6 | grand mean |
| carb | 6 | 1, 2, 3,…. | contrastable::helmert_code | NA | grand mean |
| vs | 2 | 0, 1 | contrastable::treatment_code | 1 | mean(1) |
| gear | 3 | 3, 4, 5 | stats::contr.poly | NA | grand mean |
enlist_contrasts can be used to generate a named list of
contrasts that can be used in the contrasts argument of
various modeling functions.
# Get a list of the contrasts we've explicitly set
enlist_contrasts(mtcars, contrast_schemes)#> $cyl
#> 4 8
#> 4 2/3 -1/3
#> 6 -1/3 -1/3
#> 8 -1/3 2/3
#>
#> $carb
#> <2 <3 <4 <6 <8
#> 1 -1/2 -1/3 -1/4 -1/5 -1/6
#> 2 1/2 -1/3 -1/4 -1/5 -1/6
#> 3 0 2/3 -1/4 -1/5 -1/6
#> 4 0 0 3/4 -1/5 -1/6
#> 6 0 0 0 4/5 -1/6
#> 8 0 0 0 0 5/6
#>
#> $vs
#> 0
#> 0 1
#> 1 0You can also set multiple contrasts at once using
{tidyselect} functionality.
# Create a new dataframe with a bunch of factors
my_data2 <-
data.frame(a = gl(2,10),
b = gl(5,2, ordered = TRUE),
c = gl(5,2),
d = 1:10,
e = 11:20)
enlist_contrasts(my_data2,
where(is.ordered) ~ polynomial_code,
where(is.unordered) ~ helmert_code,
d + e ~ sum_code)
#> $b
#> .L .Q .C ^4
#> 1 -6.324555e-01 0.5345225 -3.162278e-01 0.1195229
#> 2 -3.162278e-01 -0.2672612 6.324555e-01 -0.4780914
#> 3 -3.510833e-17 -0.5345225 1.755417e-16 0.7171372
#> 4 3.162278e-01 -0.2672612 -6.324555e-01 -0.4780914
#> 5 6.324555e-01 0.5345225 3.162278e-01 0.1195229
#>
#> $a
#> <2
#> 1 -0.5
#> 2 0.5
#>
#> $c
#> <2 <3 <4 <5
#> 1 -0.5 -0.3333333 -0.25 -0.2
#> 2 0.5 -0.3333333 -0.25 -0.2
#> 3 0.0 0.6666667 -0.25 -0.2
#> 4 0.0 0.0000000 0.75 -0.2
#> 5 0.0 0.0000000 0.00 0.8
#>
#> $d
#> 2 3 4 5 6 7 8 9 10
#> 1 -1 -1 -1 -1 -1 -1 -1 -1 -1
#> 2 1 0 0 0 0 0 0 0 0
#> 3 0 1 0 0 0 0 0 0 0
#> 4 0 0 1 0 0 0 0 0 0
#> 5 0 0 0 1 0 0 0 0 0
#> 6 0 0 0 0 1 0 0 0 0
#> 7 0 0 0 0 0 1 0 0 0
#> 8 0 0 0 0 0 0 1 0 0
#> 9 0 0 0 0 0 0 0 1 0
#> 10 0 0 0 0 0 0 0 0 1
#>
#> $e
#> 12 13 14 15 16 17 18 19 20
#> 11 -1 -1 -1 -1 -1 -1 -1 -1 -1
#> 12 1 0 0 0 0 0 0 0 0
#> 13 0 1 0 0 0 0 0 0 0
#> 14 0 0 1 0 0 0 0 0 0
#> 15 0 0 0 1 0 0 0 0 0
#> 16 0 0 0 0 1 0 0 0 0
#> 17 0 0 0 0 0 1 0 0 0
#> 18 0 0 0 0 0 0 1 0 0
#> 19 0 0 0 0 0 0 0 1 0
#> 20 0 0 0 0 0 0 0 0 1The functions in this package aim to be helpful when potential
mistakes are made and transparent when things happen behind the scenes
(e.g., automatic factor coercion). You can check out descriptions of
various messages and warnings in the warnings vignette with
vignette('warnings', 'contrastable').
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.