# dummyVars

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##### 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, ...)## S3 method for class 'default':
dummyVars(formula, data, sep = ".", levelsOnly = FALSE, ...)## S3 method for class 'dummyVars':
predict(object, newdata, na.action = na.pass, ...)contr.dummy(n, ...)
##### 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
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.
...
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 of 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.dummy as the base function to do this.

##### Value

• The output of dummyVars is a list of class 'dummyVars' with elements
• callthe function call
• formthe model formula
• varsnames of all the variables in the model
• facVarsnames of all the factor variables in the model
• lvlslevels of any factor variables
• sepNULL or a character separator
• termsthe terms.formula object
• levelsOnlya logical
• The predict function produces a data frame.

contr.dummy generates a matrix with n rows and n columns.

##### References

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

model.matrix, contrasts, formula

##### Aliases
• dummyVars
• dummyVars.default
• predict.dummyVars
• contr.dummy
##### 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$time) <- c("morning", "afternoon", "night") levels(when$day) <- c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")

## Default behavior:
model.matrix(~day, when)

mainEffects <- dummyVars(~ day + time, data = when)
mainEffects
predict(mainEffects, when[1:3,])

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 5.15-045, License: GPL-2

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