dummyVars

0th

Percentile

Create A Full Set of Dummy Variables

dummyVars creates a full set of dummy variables (i.e. less than full rank parameterization)

Keywords
models
Usage
dummyVars(formula, ...)
"dummyVars"(formula, data, sep = ".", levelsOnly = FALSE, fullRank = FALSE, ...)
"predict"(object, newdata, na.action = na.pass, ...)
contr.dummy(n, ...) ## DEPRECATED
contr.ltfr(n, contrasts = TRUE, sparse = FALSE)
class2ind(x, drop2nd = FALSE)
Arguments
formula
An appropriate R model formula, see References
data
A data frame with the predictors of interest
sep
An optional separator between factor variable names and their levels. Use sep = NULL for no separator (i.e. normal behavior of model.matrix as shown in the Details section)
levelsOnly
A logical; TRUE means to completely remove the variable names from the column names
fullRank
A logical; should a full rank or less than full rank parameterization be used? If TRUE, factors are encoded to be consistent with model.matrix and the resulting there are no linear dependencies induced between the columns.
object
An object of class dummyVars
newdata
A data frame with the required columns
na.action
A function determining what should be done with missing values in newdata. The default is to predict NA.
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.
x
A factor vector.
drop2nd
A logical: when the factor x has two levels, should both dummy variables be returned (drop2nd = FALSE or only the dummy variable for the first level drop2nd = TRUE.
...
additional arguments to be passed to other methods
Details

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           1      1      0      0      0      0      0
2           1      1      0      0      0      0      0
3           1      1      0      0      0      0      0
4           1      0      0      1      0      0      0
5           1      0      0      1      0      0      0
6           1      0      0      0      0      0      0
7           1      0      1      0      0      0      0
8           1      0      1      0      0      0      0
9           1      0      0      0      0      0      0

In 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      1      0      0      0      0      0
2      0      1      0      0      0      0      0
3      0      1      0      0      0      0      0
4      0      0      0      1      0      0      0
5      0      0      0      1      0      0      0
6      1      0      0      0      0      0      0
7      0      0      1      0      0      0      0
8      0      0      1      0      0      0      0
9      1      0      0      0      0      0      0

Given 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 of dummy variables.

Value

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
NULL or a character separator
terms
the terms.formula object
levelsOnly
a logical
The predict function produces a data frame.contr.ltfr generates a design matrix.

References

http://cran.r-project.org/doc/manuals/R-intro.html#Formulae-for-statistical-models

See Also

model.matrix, contrasts, formula

Aliases
  • dummyVars
  • dummyVars.default
  • predict.dummyVars
  • contr.dummy
  • contr.ltfr
  • class2ind
Examples

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"))

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)
Documentation reproduced from package caret, version 6.0-70, License: GPL (>= 2)

Community examples

Looks like there are no examples yet.