dichotomize: Indicate how categories represent a dichotomized value

Description

Multiple Response variables are Categorical Arrays in which one or more categories are set as "selected". These methods allow you to view and set that attribute.

Usage

is.dichotomized(x)
dichotomize(x, i)
undichotomize(x)
is.selected(x)
is.selected(x) <- value
# S4 method for Categories
is.dichotomized(x)
# S4 method for Categories,numeric
dichotomize(x, i)
# S4 method for Categories,logical
dichotomize(x, i)
# S4 method for Categories,character
dichotomize(x, i)
# S4 method for Categories
undichotomize(x)
# S4 method for CategoricalVariable,ANY
dichotomize(x, i)
# S4 method for CategoricalArrayVariable,ANY
dichotomize(x, i)
# S4 method for CategoricalVariable
undichotomize(x)
# S4 method for CategoricalArrayVariable
undichotomize(x)
# S4 method for Categories
is.selected(x)
# S4 method for Categories
is.selected(x) <- value
# S4 method for Category
is.selected(x)
# S4 method for Category
is.selected(x) <- value

Value

Categories or the Variable, (un)dichotomized accordingly

Arguments

x: Categories or a Variable subclass that has Categories
i: For the dichotomize methods, the numeric or logical indices of the categories to mark as "selected", or if character, the Category "names". Note that unlike some other categorical variable methods, numeric indices are positional, not with reference to category ids.
value: For is.selected<-, A logical vector indicating whether the category should be selected. For a single category the value should be either TRUE or FALSE. To change the selection status for a Categories object, supply a logical vector which is the same length as the number of categories.

Details

dichotomize lets you specify which categories are "selected", while undichotomize strips that selection information. Dichotomize converts a Categorical Array to a Multiple Response, and undichotomize does the reverse. is.dichotomized reports whether categories have any selected values.

is.selected is lower level and maps more directly onto the "selected" attributes of categories. The best illustration of this difference is that is.selected(categories(var)) returns a logical vector, a value for each category, while is.dichotomized(categories(var)) returns a single TRUE/FALSE value.

Examples

Run this code

if (FALSE) {
ds <- newExampleDataset()
is.MR(ds$allpets)
is.dichotomized(categories(ds$allpets))
is.selected(categories(ds$allpets))
ds$allpets <- undichotomize(ds$allpets)
is.CA(ds$allpets)
ds$allpets <- dichotomize(ds$allpets, "selected")
is.MR(ds$allpets)
}