Contrast function for one-hot encodings
This contrast function produces a model matrix with indicator columns for each level of each factor.
contr_one_hot(n, contrasts = TRUE, sparse = FALSE)
n |
A vector of character factor levels or the number of unique levels. |
contrasts |
This argument is for backwards compatibility and only the
default of |
sparse |
This argument is for backwards compatibility and only the
default of |
By default, model.matrix() generates binary indicator variables for
factor predictors. When the formula does not remove an intercept, an
incomplete set of indicators are created; no indicator is made for the
first level of the factor.
For example, species and island both have three levels but
model.matrix() creates two indicator variables for each:
library(dplyr) library(modeldata) data(penguins) levels(penguins$species)
## [1] "Adelie" "Chinstrap" "Gentoo"
levels(penguins$island)
## [1] "Biscoe" "Dream" "Torgersen"
model.matrix(~ species + island, data = penguins) %>% colnames()
## [1] "(Intercept)" "speciesChinstrap" "speciesGentoo" "islandDream" ## [5] "islandTorgersen"
For a formula with no intercept, the first factor is expanded to indicators for all factor levels but all other factors are expanded to all but one (as above):
model.matrix(~ 0 + species + island, data = penguins) %>% colnames()
## [1] "speciesAdelie" "speciesChinstrap" "speciesGentoo" "islandDream" ## [5] "islandTorgersen"
For inference, this hybrid encoding can be problematic.
To generate all indicators, use this contrast:
# Switch out the contrast method
old_contr <- options("contrasts")$contrasts
new_contr <- old_contr
new_contr["unordered"] <- "contr_one_hot"
options(contrasts = new_contr)
model.matrix(~ species + island, data = penguins) %>%
colnames()
## [1] "(Intercept)" "speciesAdelie" "speciesChinstrap" "speciesGentoo" ## [5] "islandBiscoe" "islandDream" "islandTorgersen"
options(contrasts = old_contr)
Removing the intercept here does not affect the factor encodings.
A diagonal matrix that is n-by-n.
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