# dummyVars

##### 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,
fullRank = FALSE, ...)

## S3 method for class 'dummyVars':
predict(object, newdata, na.action = na.pass, ...)

contr.dummy(n, ...) ## DEPRECATED

contr.ltfr(n, contrasts = TRUE, sparse = 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 de - 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.
- ...
- 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.ltfr`

as the base function to do this.

##### 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 separatorterms the `terms.formula`

objectlevelsOnly a logical - The
`predict`

function produces a data frame.`contr.ltfr`

generates a design matrix.

##### References

##### See Also

##### 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,])
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 6.0-35, License: GPL-2*