Create A Full Set of Dummy Variables
dummyVars creates a full set of dummy variables (i.e. less than full
rank parameterization)
dummyVars(formula, ...) ## Default S3 method: dummyVars(formula, data, sep = ".", levelsOnly = FALSE, fullRank = FALSE, ...) ## S3 method for class 'dummyVars' print(x, ...) ## S3 method for class 'dummyVars' predict(object, newdata, na.action = na.pass, ...) contr.ltfr(n, contrasts = TRUE, sparse = FALSE) class2ind(x, drop2nd = FALSE)
formula |
An appropriate R model formula, see References |
... |
additional arguments to be passed to other methods |
data |
A data frame with the predictors of interest |
sep |
An optional separator between factor variable names and their
levels. Use |
levelsOnly |
A logical; |
fullRank |
A logical; should a full rank or less than full rank
parameterization be used? If |
x |
A factor vector. |
object |
An object of class |
newdata |
A data frame with the required columns |
na.action |
A function determining what should be done with missing
values in |
n |
A vector of levels for a factor, or the number of levels. |
contrasts |
A logical indicating whether contrasts should be computed. |
sparse |
A logical indicating if the result should be sparse. |
drop2nd |
A logical: if the factor has two levels, should a single binary vector be returned? |
Most of the contrasts functions in R produce full rank
parameterizations of the predictor data. For example,
contr.treatment creates a reference cell in the data
and defines dummy variables for all factor levels except those in the
reference cell. For example, if a factor with 5 levels is used in a model
formula alone, contr.treatment creates columns for the
intercept and all the factor levels except the first level of the factor.
For the data in the Example section below, this would produce:
(Intercept) dayTue dayWed dayThu dayFri daySat daySun
1 0 0 0 0 0 0
1 0 0 0 0 0 0
1 0 0 0 0 0 0
1 0 1 0 0 0 0
1 0 1 0 0 0 0
1 0 0 0 1 0 0
1 0 0 0 0 1 0
1 0 0 0 0 1 0
1 0 0 0 1 0 0In some situations, there may be a need for dummy variables for all the levels of the factor. For the same example:
dayMon dayTue dayWed dayThu dayFri daySat daySun
1 0 0 0 0 0 0
1 0 0 0 0 0 0
1 0 0 0 0 0 0
0 0 1 0 0 0 0
0 0 1 0 0 0 0
0 0 0 0 1 0 0
0 0 0 0 0 1 0
0 0 0 0 0 1 0
0 0 0 0 1 0 0Given a formula and initial data set, the class dummyVars gathers all
the information needed to produce a full set of dummy variables for any data
set. It uses contr.ltfr as the base function to do this.
class2ind is most useful for converting a factor outcome vector to a
matrix (or vector) of dummy variables.
The output of dummyVars is a list of class 'dummyVars' with
elements
call |
the function call |
form |
the model formula |
vars |
names of all the variables in the model |
facVars |
names of all the factor variables in the model |
lvls |
levels of any factor variables |
sep |
|
terms
|
the |
levelsOnly |
a logical |
The predict function produces a data frame.
class2ind returns a matrix (or a vector if drop2nd = TRUE).
contr.ltfr generates a design matrix.
contr.ltfr is a small modification of
contr.treatment by Max Kuhn
when <- data.frame(time = c("afternoon", "night", "afternoon",
"morning", "morning", "morning",
"morning", "afternoon", "afternoon"),
day = c("Mon", "Mon", "Mon",
"Wed", "Wed", "Fri",
"Sat", "Sat", "Fri"),
stringsAsFactors = TRUE)
levels(when$time) <- list(morning="morning",
afternoon="afternoon",
night="night")
levels(when$day) <- list(Mon="Mon", Tue="Tue", Wed="Wed", Thu="Thu",
Fri="Fri", Sat="Sat", Sun="Sun")
## Default behavior:
model.matrix(~day, when)
mainEffects <- dummyVars(~ day + time, data = when)
mainEffects
predict(mainEffects, when[1:3,])
when2 <- when
when2[1, 1] <- NA
predict(mainEffects, when2[1:3,])
predict(mainEffects, when2[1:3,], na.action = na.omit)
interactionModel <- dummyVars(~ day + time + day:time,
data = when,
sep = ".")
predict(interactionModel, when[1:3,])
noNames <- dummyVars(~ day + time + day:time,
data = when,
levelsOnly = TRUE)
predict(noNames, when)
head(class2ind(iris$Species))
two_levels <- factor(rep(letters[1:2], each = 5))
class2ind(two_levels)
class2ind(two_levels, drop2nd = TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.