caret (version 6.0-73)

dummyVars: Create A Full Set of Dummy Variables

Description

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

Usage

dummyVars(formula, ...)
"dummyVars"(formula, data, sep = ".", levelsOnly = FALSE, fullRank = FALSE, ...)
"print"(x, ...)
"predict"(object, newdata, na.action = na.pass, ...)
contr.ltfr(n, contrasts = TRUE, sparse = FALSE)

Arguments

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 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.

x

A factor vector.

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.

Value

The output of dummyVars is a list of class 'dummyVars' with elements elementsThe predict function produces a data frame.contr.ltfr generates a design matrix.

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.

References

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

See Also

model.matrix, contrasts, formula

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)