---
title: "Contrastable Overview"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Contrastable Overview}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
This vignette describes some of the contrast coding helpers this package provides.
It should be noted that this package does not seek to provide an interface for
creating hypothesis matrices and their respective contrast matrices. For that
functionality you might look into the `multcomp` or `hypr` packages. The contrast
functionality of this package is focused on the following:
- Easily implement commonly used contrast coding schemes
- Make reference levels explicit
- Cut down on repetitive coding involved in setting contrasts
- Retain interpretable labels in model summary coefficients
- Allow the implementation of custom contrasts (but again not their creation)
This vignette will not go into detail how different contrast schemes are derived or
interpreted, but will describe some of the high level differences. I recommend
reading the following:
- [UCLA R Library Contrast Coding Systems for Categorical Variables](https://stats.oarc.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/)
- [How to capitalize on a priori contrasts in linear (mixed) models: A tutorial](https://doi.org/10.1016/j.jml.2019.104038)
- [Regresion modeling for linguistic data (preprint)](https://osf.io/pnumg/)
I also have a few blog posts about contrast coding; if you need further
explanation of contrasts and more working examples beyond this vignette, read
the first one below:
- [Introduction to Contrasts using Contrastable](https://tsostaricsblog.netlify.app/posts/contrastable/)
- [On Helmert Coding](https://tsostaricsblog.netlify.app/posts/helmert/)
```{r setup, message=FALSE, warning=FALSE}
library(contrastable)
library(dplyr)
library(MASS)
```
## Introduction to contrasts
Statistical models such as linear regression need numbers to operate with,
but often we have discrete categories that we want to model such as ethnicity,
country, treatment group, etc.
Typically, we have some comparisons in mind before we do our statistics:
compare the group receiving treatment to a reference level who did not, or
something like compare group A to group B.
**Contrast coding** is the process of assigning numbers that encode the
comparisons we want to make prior to fitting a statistical model.
What this allows us to do is strategically set up our model to be interpretable
in a way that's meaningful to us even though the *fit* of the statistical model
is unchanged.
So, contrast coding is important for inferential statistics and reporting
coefficients correctly, but is not that important for group-level predictions.
Throughout this vignette and package, I refer to different **contrast schemes**,
which I define as *an encoding of a principled set of comparisons for an arbitrary number of factor levels*.
The important bit here is the *arbitrary* number part.
To start with an example, consider an experiment where people are tasked with
keeping track of the location of different shapes under different levels of
cognitive load, such as needing to remember different strings of numbers.
Basically, a change detection task with a digit span cognitive load manipulation.
We'll call the levels of this *cognitive load* factor none, low, and high.
A priori, the speed with which people detect a shape that changes location should
vary depending on the cognitive load.
Specifically, reaction time (RT) should be slower when cognitive load is higher.
What kinds of comparisons could we make, and how do we encode these in our
statistical model?
One comparison might be to compare low to none, then high to none.
Another could be to compare low to none, then high to *low*.
Yet another could be to compare low to none, then high to *low and none* together.
Or, we could compare each level to the average across the conditions.[^dfnote]
Specifying these different comparisons, which will really just amount to differences in group means, is our goal here.
In our example, what if we went found that our high cognitive load condition
wasn't high enough?
What if we needed to add an extra-high level?
We could add a comparison of extra-high to none, or compare extra-high to high.
Or, we could compare extra-high to none plus low plus high together.
Ideally, if we're going for pairwise comparisons from a reference level,
successive differences between levels, or some kind of nested set of comparisons,
we would want to be able to specify this consistently without needing to revisit every instance of specifying our comparisons just to accommodate the new level.
[^dfnote]: Technically, due to degrees of freedom, we'd compare only 2 of the
3 levels to the average.
Throughout this vignette and package, I refer to different **contrast schemes**,
which I define as *an encoding of a principled set of comparisons for an arbitrary number of factor levels*.
The important bit here is the *arbitrary* number part such that if the goal
is to take pairwise comparisons from a reference level, then adding a new level
should only amount to encoding an additional pairwise comparison to the same
reference level.
I'll contrast this approach to a more manual approach to contrast coding, where
you would explicitly write out, in code, every single comparison for every
single factor you want to make.
Consider if we added a new three-level predictor crossed with our four level
example from before (we'll call the new predictor *group* with levels A, B, C).
If we also just wanted to do pairwise comparisons, a scheme approach would
encode *pairwise comparisons* for cognitive load and *pairwise comparisons*
for the new factor.
The manual approach would say to encode low-none, high-none, and extra high-none;
then, encode B-A, and C-A.
If we introduce a new group D later, we have to remember to go in and specify
D-A too.
So, while the manual approach is maximally explicit about exactly what we want
to compare, it's also more things to write out and more chances that we make a
mistake while implementing it.
Given that contrasts are specified using matrices in R, the chances of a mistake
are high.
Accordingly, R provides a few functions that implement common contrast schemes
such as `contr.treatment` and `contr.sum`, which return matrices given a number
of levels.
However, there's some trickiness with them.
Below is a quick list of some issues this package attempts to circumvent; if
you're familiar with contrast coding from your own work, some of this may sound
familiar and some of it may surprise you.
- When setting contrasts to a factor that differ from the R session's default
contrasts, the labels for the comparisons in the output of the statistical model
are reset. This requires the researcher to keep track of which numeric indices
(1, 2, etc.) correspond to which comparisons---which also requires keeping
track of the alphabetical order of the levels. Alternatively, the researcher
can set the comparison labels using `dim()`, `dimnames()`, or `colnames()`.
In my experience, I rarely see people in my field do this, and so I believe it
would be naive to expect people to suddenly (learn how to and) start doing so.
- The default contrast scheme, `contr.treatment`, places the
reference level as the first level alphabetically while `contr.sum` places the
reference level as the LAST level alphabetically. To verify for yourself, try
running `contr.treatment(5)` and `contr.sum(5)`. respectively, the reference
levels are identifiable by the rows containing either all 0s or all -1s.
- Related to the above, when people want to use $\pm.5$ for their contrasts
for a two-level factor, it is not uncommon to see something like
`-contr.sum(2)/2`. However, this ***does not generalize*** when the number of
levels is greater than 2, i.e,. `-contr.sum(3)/2` will not give you what you expect.
- `contr.helmert` returns unscaled matrices, giving effect estimates that need
to be manually scaled. Tests of statistical significance are still meaningful,
but interpreting the magnitude of effects using matrices from this function
are likely to be incorrect if not scaled manually.
- Quite a few contrasts simplify to $\pm.5$ when there are two levels. For
example, helmert coding and backward difference coding both give +.5 and -.5.
But, if we were to add a third level to these, the values for the newly added
comparison are NOT the same. Accordingly, if we're not explicit about what the
**goal** of the comparisons are (nested comparisons vs successive comparisons)
then future researchers following up on our work might attempt to use the same
contrast scheme but not understand what the new comparison is.
## Motivation
I'm going to use the mtcars dataset just to have something to work with.
We'll convert a bunch of the columns to factors up front.
```{r mdl-data}
mdl_data <-
mtcars |>
as_tibble() |>
mutate(cyl = factor(cyl),
twolevel = factor(rep(c("a", "b"), times = nrow(mtcars) / 2)),
gear = factor(gear),
carb = factor(carb))
```
We can inspect the contrasts for the factors using `contrasts()`, which will use
the default contrasts for unordered factors
```{r inspect-contrasts}
options("contrasts") # Show defaults for unordered and ordered
contrasts(mdl_data$twolevel)
contrasts(mdl_data$carb) # Note the reference level
```
We can see that the levels for `carb` are reflected in the coefficient labels:
```{r default-model-summary}
summary(lm(mpg ~ carb, data = mdl_data))
```
But if we changed the contrasts ourselves those helpful labels go away and are
replaced with numeric indices. Note that the level names were originally
also numbers, so it would be easy to get mixed up at this point.
```{r manual-change-contrasts}
mdl_data2 <- mdl_data
contrasts(mdl_data2$carb) <- contr.sum(6)
contrasts(mdl_data2$carb) # Note the reference level
summary(lm(mpg ~ carb, data = mdl_data2))
```
## Usage
Now I'll introduce the main workhorse of this package: the `set_contrasts`
function.
This function features a special syntax to quickly set contrast schemes.
I'll introduce features one at a time.
First, we'll set `carb` to use sum coding as we did before.
We do this using two-sided formulas, where the left hand side of the formula
is a column in our dataset and the right hand side includes information about
the contrasts we want to set.
For example, this takes the form `varname ~ code_by`.
Below, we specify that we want `carb` to use contrasts generated by the `sum_code`
function.
Two things to note: `set_contrasts` will ***automatically*** coerce specified
columns into factors, so you don't need to worry about calling `factor()` on
columns first.
Note that the function will also give messages about other factors that you
haven't set explicitly.
```{r intro-set-contrasts}
mdl_data3 <- set_contrasts(mdl_data, carb ~ sum_code)
contrasts(mdl_data2$carb) # matrix from before
contrasts(mdl_data3$carb) # new matrix, note the column names
```
Note that, unlike last time, the reference level is now the first level
alphabetically and the labels are retained (compare the contrast matrices)
For schemes that have a singular reference level, setting contrasts with
`set_contrasts` will always set the reference level to the first level
alphabetically.
Currently, the reference level for `carb` is `1`.
If we want to change the reference level, we use the `+` operator in our formula.
The change to the formula looks like `varname ~ code_by + reference`.
Let's change it to `3`:
```{r set-reference}
mdl_data4 <- set_contrasts(mdl_data, carb ~ sum_code + "3")
contrasts(mdl_data4$carb)
```
Note that when using sum coding, the intercept will be the mean of the group
means:
```{r intercept-example}
# mean of group means:
group_means <- summarize(mdl_data4, grp_mean = mean(mpg), .by = "carb")
group_means
mean(group_means$grp_mean)
# model coefficients
coef(lm(mpg ~ carb, data = mdl_data4))
```
Let's say, hypothetically, we wanted to get differences of each comparison
level to the grand mean, but we also wanted the intercept to be not the
grand mean but the mean of our reference level?
We can set this using the `*` operator:
`varname ~ code_by + reference * intercept`.
```{r set-intercept}
mdl_data5 <- set_contrasts(mdl_data, carb ~ sum_code + 3 * 3)
contrasts(mdl_data5$carb)
coef(lm(mpg ~ carb, data = mdl_data5))
```
Finally, let's say we wanted labels that were a bit more clear about what the
comparisons are here.
We can set the label names ourselves using the `|` operator:
`varname ~ code_by + reference * intercept | labels`
```{r set-labels}
mdl_data6 <- set_contrasts(mdl_data,
carb ~ sum_code + 3 * 3 | c("1-3",
"2-3",
"4-3",
"6-3",
"8-3"))
contrasts(mdl_data5$carb)
coef(lm(mpg ~ carb, data = mdl_data6))
```
Here's what it would look like if you tried to manually do this yourself.
It's highly unlikely you would correctly specify every single value
and every single label correctly on the first go (I actually messed up myself
while writing out the matrix).
```{r manual-matrix}
mdl_data7 <- mdl_data
my_contrasts <- matrix(c(2, 1, 0, 1, 1, 1,
1, 2, 0, 1, 1, 1,
1, 1, 0, 2, 1, 1,
1, 1, 0, 1, 2, 1,
1, 1, 0, 1, 1, 2),
nrow = 6)
dimnames(my_contrasts) <-
list(
c("1", "2", "3", "4", "6", "8"),
c("1-3", "2-3", "4-3", "6-3", "8-3")
)
contrasts(mdl_data7$carb) <- my_contrasts
contrasts(mdl_data7$carb)
```
To see how this can get even more error prone, consider a contrast matrix like
the one below.
Personally I would not trust myself to get the matrix correct, and if I ever
had to add a level to the design it would be obscene to try and edit it.
```{r matrix-example-fractions}
mdl_data8 <- set_contrasts(mdl_data, carb ~ helmert_code * 4)
MASS::fractions(contrasts(mdl_data8$carb))
```
The work would compound when we have multiple factors, but with the
`set_contrasts` function we can specify what our desired scheme is easily:
```{r set-contrasts-multiple-vars}
mdl_data9 <- set_contrasts(mdl_data,
carb ~ helmert_code * 4,
cyl ~ scaled_sum_code + 4,
twolevel ~ scaled_sum_code + "a",
gear ~ helmert_code)
MASS::fractions(contrasts(mdl_data9$carb))
MASS::fractions(contrasts(mdl_data9$cyl))
MASS::fractions(contrasts(mdl_data9$twolevel))
MASS::fractions(contrasts(mdl_data9$gear))
```
Practically speaking, the most common things you'll be doing is setting
a contrast scheme and maybe changing the reference level, so the `+` operator
is very important.
### enlist_contrasts
A related function is the `enlist_contrasts` function, which follows the same
exact rules as `set_contrasts` but will return a list of contrasts instead of
setting the contrasts to the data itself.
Henceforth, when I'm not going to use the dataframe in an example, I'll just use
this to show the contrast matrices.
```{r intro-enlist-contrasts}
enlist_contrasts(mdl_data,
carb ~ helmert_code * 4,
cyl ~ scaled_sum_code + 4)
```
### Contrast functions
I showed a few functions that generate contrasts like `sum_code` and
`scaled_sum_code` and `helmert_code`.
Below is a list of functions provided by this package:
- `treatment_code`: Pairwise comparisons to a reference level, intercept equals
the reference level (equivalent to `contr.treatment`)
- `sum_code`: Comparisons to the grand mean, intercept is the grand mean
(equivalent to `contr.sum`, but reference level is the first level)
- `scaled_sum_code`: Pairwise comparisons to a reference level, intercept
equals the grand mean
- `helmert_code`: Nested comparisons starting from the first level. Note
this is NOT equivalent to `contr.helmert`, as the latter gives an unscaled
matrix.
- `reverse_helmert_code`: Nested comparisons starting from the last level.
- `backward_difference_code`: Successive differences. For levels A, B, C,
gives the differences B-A and C-B
- `forward_difference_code`: Successive differences. For levels A, B, C,
gives the differences A-B and B-C.
- `cumulative_split_code`: Dichotomous differences. For levels A, B, C, D,
gives the differences A-(B+C+D), (A+B)-(C+D), (A+B+C)-D.
- `orth_polynomial_code`: Orthogonal polynomial coding, equivalent to
`contr.poly`.
- `raw_polynomial_code`: Raw polynomial coding, I do not recommend using this
unless you know what you're getting into.
- `polynomial_code`: An alias for `orthogonal_polynomial_code`
You can also use contrast functions from any other package so long as the
first argument is the number of levels used to generate the matrix.
However, again, if there's a singular level used for the reference level, this
will always be set to the first level regardless of what the original matrix was.
```{r example-functions}
# all equivalent to carb ~ sum_code
foo <- sum_code
enlist_contrasts(mdl_data, carb ~ contr.sum)
enlist_contrasts(mdl_data, carb ~ sum_code)
enlist_contrasts(mdl_data, carb ~ foo)
```
If you want to suppress this behavior, you can use the wrapper `I` from base R.
Note this will also result in not setting the labels for you: it will use
the generated matrix as-is.
This is useful if you explicitly create a matrix and
want to make sure the reference level isn't shifted around.
```{r example-as-is}
enlist_contrasts(mdl_data, carb ~ I(contr.sum))
```
One last quick note is that you can keep the parentheses with the function,
so if your tab-autocomplete puts parentheses you don't have to worry about it.
```{r parentheses-handling}
enlist_contrasts(mdl_data, carb ~ contr.sum())
```
However, the function does need to exist; the following won't run:
```{r function-must-exist,eval = FALSE}
foo <- contr.sum(6) # foo is a matrix
enlist_contrasts(mdl_data, carb ~ foo()) # foo is not a function
```
### tidyselect functions
If you know you're going to use the same scheme for multiple variables, but
don't need to set the reference level or labels for any one in particular, you
can use tidyselect functionality to set multiple contrasts.
I'll show a few examples of how to do this.
First, you can specify multiple variables on the left hand side using the `+`
operator.
```{r lhs-plus}
# equivalent to: enlist_contrasts(mdl_data, cyl ~ sum_code, gear ~ sum_code)
enlist_contrasts(mdl_data, cyl + gear ~ sum_code)
```
You can also use selecting functions like the below:
```{r tidyselect-where}
enlist_contrasts(mdl_data, where(is.factor) ~ sum_code)
# see also enlist_contrasts(mdl_data, where(is.unordered) ~ sum_code)
# see also enlist_contrasts(mdl_data, where(is.numeric) ~ sum_code)
```
This also lets you programmatically set contrasts like so:
```{r tidyselect-all-of}
these_vars <- c("cyl", "gear")
enlist_contrasts(mdl_data, all_of(these_vars) ~ sum_code)
```
However, you have to ensure that no variables are duplicated. The following
are not allowed regardless of whether the right right hand sides evaluate to
the same contrast schemes.
```{r tidyselect-forbidden, eval = FALSE}
enlist_contrasts(mdl_data,
cyl ~ sum_code,
where(is.factor) ~ sum_code) # cyl is a factor for mdl_data
enlist_contrasts(mdl_data,
cyl ~ sum_code,
cyl + gear ~ sum_code) # cyl can't be specified twice
```
You can check out the tidyselect package for more information.
`is.unordered` is provided in this package as an analogue to `is.ordered`.
### Other ways to set contrasts
You can also set contrasts using things other than a function.
Specifically, you can pass the following classes to `code_by` in the two-sided
formulas:
- `array`
- `matrix`
- `hypr`
- `function` (must return a contrast matrix, cannot be an anonymous function)
- `symbol` (must evaluate to a contrast matrix though)
If you're using a custom matrix for a tricky analysis, then you can specify
the matrix yourself and then pass it to set_contrasts:
```{r use-matrix}
my_matrix <- contr.sum(6) / 2
enlist_contrasts(mdl_data, carb ~ my_matrix)
```
You can also specify a list of formulas and pass that to different functions.
```{r use-list}
my_contrasts <- list(carb ~ contr.sum, gear ~ scaled_sum_code)
enlist_contrasts(mdl_data, my_contrasts)
mdl_data12 <- set_contrasts(mdl_data, my_contrasts)
```
This functionality is very important for the next function I'll discuss.
### glimpse_contrasts
It can be very useful to get a summary of the different factors and their
contrasts in your dataset.
The `glimpse_contrasts` function does this for you by passing it your dataset
and your contrasts.
```{r intro-glimpse-contrasts}
my_contrasts <- list(carb ~ contr.sum,
gear ~ treatment_code + 4,
twolevel ~ scaled_sum_code * "b",
cyl ~ helmert_code)
mdl_data$twolevel
enlist_contrasts(mdl_data, cyl ~ scaled_sum_code + 6 * 6)
mdl_data13 <- set_contrasts(mdl_data, my_contrasts)
glimpse_contrasts(mdl_data13, my_contrasts)
```
Note that if you haven't actually set your contrasts to the dataset, you'll
receive a series of warnings:
```{r glimpse-warnings}
glimpse_contrasts(mdl_data, my_contrasts)
```
You can also specify the formulas directly, like with `set_contrasts` and
`enlist_contrasts`--- but usually you'll want to have a list of formulas and
pass this to the different functions.
Note how we retyped everything we typed above just to get the summary table.
```{r glimpse-manual}
glimpse_contrasts(mdl_data13,
carb ~ contr.sum,
gear ~ treatment_code + 4,
twolevel ~ scaled_sum_code * "b",
cyl ~ helmert_code)
```
If you don't provide any contrast information, `glimpse_contrasts` will assume
that factors should be using their default contrasts.
If the contrasts are not the same as the defaults, you'll receive warnings:
```{r glimpse-check-default}
glimpse_contrasts(mdl_data) # no warnings
glimpse_contrasts(mdl_data13) # warnings
```
If there are mismatches between the data and the contrasts specified by the
formulas, you'll also get warnings:
```{r glimpse-mismatch-warnings}
glimpse_contrasts(mdl_data,
carb ~ contr.sum,
gear ~ treatment_code * 4,
cyl ~ contr.treatment | c("diff1", "diff2"))
```
Remember that `set_contrasts` will automatically coerce dataframe columns into
factors when specifying a contrast, but `glimpse_contrasts` does *not* because
it does not modify the provided dataframe.
### Note on polynomial contrasts
There is an additional operator for the formulas that only applies to polynomial
contrasts.
For background, polynomial contrasts encode polynomial "trends" across the
levels of a factor.
So, testing for a linear-like trend, a quadratic-like trend, etc.
One thing you *can* do, though I don't necessarily *recommend* it is removing
higher-order trends.
For instance, if you have 8 levels but don't want to bother with polynomials
over degree 3 (even though you'll get up to degree 7 for "free").
In this situation you can use the `-` operator:
`varname ~ code_by + reference * intercept - low:high | labels`
```{r drop-trends}
enlist_contrasts(mdl_data, carb ~ contr.poly)
enlist_contrasts(mdl_data, carb ~ contr.poly - 3:5)
```
Note that you can ONLY use this with `enlist_contrasts` and `glimpse_contrasts`.
This is because if you remove columns from a contrast matrix, R will fill them
back with something else.
```{r drop-trends-reinstated-matrix}
mdl_data14 <- set_contrasts(mdl_data, carb ~ contr.sum)
contrasts(mdl_data14$carb)
contrasts(mdl_data14$carb) <- contrasts(mdl_data14$carb)[, 1:2]
contrasts(mdl_data14$carb)
```
What do these values correspond to?
If we check out the hypothesis matrix, we can see that it seems like it's trying
to encode 0 but with some floating point error:
```{r drop-trends-hypotheses-floats}
MASS::fractions(
contrastable:::.convert_matrix(
contrasts(mdl_data14$carb)
)
)
```
This is why I don't recommend actually using the `-` operator.
If you use something like `lm` to fit functions, you can use the output
from `enlist_contrasts` as the argument to `contrasts`.
Although, I'm pretty sure the model matrix will just add in these values anyways.
If you try to use the `-` operator with `set_contrasts` you'll get a warning,
and the `-` operator will be ignored:
```{r drop-trends-set-contrasts-incompatible}
mdl_data15 <- set_contrasts(mdl_data, carb ~ polynomial_code - 3:5)
```
## decompose_contrasts
This is somewhat related to the `-` operator described above, but has more uses.
When considering a factor with 3 levels, you'll end up with two coefficients in
your model.
In other words, a single factor is "decomposed" behind the scenes into multiple
numeric predictors for the model matrix.
Sometimes it can be helpful to work directly with these decomposed predictors.
For example, interactions in GAM models with `mgcv` can often be a pain to work
with, and sometimes it's easier to just group the interaction of two predictors
into a series of individual predictors.
However, doing the matrix multiplication one at a time (or trying to
conceptualize how the matrices fit together) can be difficult, especially when
R's model matrix functionality can do it for us.
`decompose_contrasts` provides an interface to do such decomposition.
```{r}
mdl_data16 <-
mdl_data |>
set_contrasts(cyl ~ helmert_code,
gear ~ helmert_code) |>
decompose_contrasts(~ cyl * gear)
# Look at the decomposed contrast columns
mdl_data16 |>
dplyr::select(matches("^cyl|gear")) |>
head()
```
The column names take the name from the labels, so these may not always be
easy to work with when there are special characters.
You can wrangle these or use something like the `{janitor}` package as needed.
Here's a comparison of using the `cyl` predictor normally (which gives us
two coefficients), and specifying the comparisons separately after decomposing
the contrasts:
```{r}
coef(lm(mpg ~ cyl, data = mdl_data16))
coef(lm(mpg ~ `cyl<6` + `cyl<8`, data = mdl_data16))
coef(lm(mpg ~ cyl * gear, data = mdl_data16))
coef(lm(mpg ~
`cyl<6` + `cyl<8` +
`gear<4` + `gear<5` +
`cyl<6:gear<4` +
`cyl<8:gear<4` +
`cyl<6:gear<5` +
`cyl<8:gear<5`,
data = mdl_data16))
```
If we *really* wanted to, we could use just one of the comparisons, but note
that this may give misleading results:
```{r}
coef(lm(mpg ~ `cyl<6` + `cyl<8`, data = mdl_data16))
coef(lm(mpg ~ `cyl<6`, data = mdl_data16)) # not the same estimate as the above
```
This function can be especially valuable for pedagogical purposes, as it
shows how the qualitative labels in the factor data is converted into numeric
values for the model to work with.
## Typical patterns
There are two general patterns you'll take in analyses with this package:
### Pattern 1: No glimpsing
Here's a general usage pattern when you don't want to use `glimpse_contrasts`.
```{r usage-pattern-1}
raw_data <- mtcars # load raw data from a csv or something here
# wrangle data for your final model
final_data <-
mtcars |>
# mutate(# ... some data wrangling transformations ..) |>
set_contrasts(carb ~ sum_code) # set contrasts at the very end
mdl <- lm(mpg ~ carb, data = final_data) # run model with contrasts set
```
### Pattern 2: With information about contrasts
Here's a usage pattern if you wanted to, say, publish the contrast matrices
or report the glimpse table in a publication or preregistration:
```{r usage-pattern-2}
raw_data <- mtcars # load raw data from a csv or something here
# specify contrasts up front
my_contrasts <- list(carb ~ sum_code,
cyl ~ scaled_sum_code,
gear ~ sum_code)
# wrangle data for your final model
final_data <-
mtcars |>
# mutate(# ... some data wrangling transformations ..) |>
set_contrasts(my_contrasts) # set contrasts at the very end
# Show the matrices we're using
enlist_contrasts(final_data, my_contrasts)
# Show a summary
glimpse_contrasts(final_data, my_contrasts)
# Fit the model with contrasts set
mdl <- lm(mpg ~ carb, data = final_data)
```
If you want to show fractions instead of decimals, use `MASS::fractions` with
each element of the list of contrasts:
```{r fractions-usage}
enlist_contrasts(final_data, my_contrasts) |>
lapply(MASS::fractions)
```
If you use the `targets` package, you can set up the list of contrasts as its
own target, then pass that to other targets so that your analyses will be rerun
whenever you change the contrasts (though this shouldn't happen frequently).
## Conclusion
I discussed the main functions in this package and the special syntax that
is implemented to set contrasts.
If you want additional examples, see my blog posts below:
- [Introduction to Contrasts using Contrastable](https://tsostaricsblog.netlify.app/posts/contrastable/)
- [On Helmert Coding](https://tsostaricsblog.netlify.app/posts/helmert/)
If you need additional information regarding the various warnings and messages
this package throws, you can check out the `warnings` vignette in this package.
## Citation examples
When citing the package in a paper, ideally three things are achieved:
- Mention the contrastable R package (Sostarics, 2024)
- Mention which contrast scheme is used for each variable with reference levels
as appropriate
- Disambiguate the term "sum coding" when used
In a paper with multiple analyses, these don't necessarily need to all be
mentioned on each analysis, especially when there are commonalities between
them.
Bad *on first mention*: "Condition and group are sum coded." (this can be fine if "sum code" is defined previously)
Good: "Condition is coded using the `sum_code()` function from the contrastable package (Sostarics 2024), using A as the reference level. Group is similarly sum coded,
with X as the reference level."
Also good: "For all of our analyses, we use the contrastable package (Sostarics, 2024) to set the contrasts for our categorical variables. Condition and group are sum coded (reference level -1, comparisons +1) with A and X as the reference levels, respectively."
The point here is to disambiguate what is meant by "sum code", which has inconsistent usage in the literature.
Here's a paragraph example describing two models:
A bit repetitive: "In the model for Experiment 1, Condition is treatment coded
using the `treatment_code()` function from the contrastable package (Sostarics, 2024),
with A as the reference, and Group is scaled sum coded using the `scaled_sum_code()`
function from the contrastable package, with X as the reference.
In the model for Experiment 2, Condition is treatment coded with the `treatment_code()` function (reference=A) and Group is scaled sum coded with `scaled_sum_code()` (reference=X), while the additional Context predictor is scaled sum coded using the `scaled_sum_code()` function (reference=NoContext)."
Rewritten: "We use the treatment_code() and scaled_sum_code() functions from the
contrastable package (Sostarics, 2024) when setting the contrasts for our
categorical variables. In the model for Experiment 1, Condition is treatment
coded (reference=A) and Group is scaled sum coded (reference=X). For Experiment 2,
the additional Context predictor is scaled sum coded (reference=NoContext);
as in Exp. 1, Condition is treatment coded (reference=A) and Group is sum coded (reference=X)."
Other examples:
- For all variables, contrasts were set using the `scaled_sum_code()` function from the contrastable package (Sostarics 2024).
- We use the contrastable package (Sostarics 2024) for contrast coding our
categorical variables; details of the contrasts for each model are provided in
the appendix. (be sure to do the latter!)
- Below are the contrast matrices returned by the `helmert_code()` and
`scaled_sum_code()` functions from the contrastable R package (Sostarics 2024)
- We use the `set_contrasts()` and `sum_code()` functions from the contrastable package
(Sostarics 2024) to sum code (+1/-1) our variables
I also recommend writing out, potentially in a footnote, what the comparisons are.
Good: "We use the contrastable package's `sum_code()` function (+1/-1, Sostarics 2024)
for all categorical variables."
Better: "We use the contrastable package's `sum_code()` function (+1/-1, Sostarics 2024)
for all categorical variables.
This contrast scheme encodes differences between each comparison level and the grand mean."