# setOldClass

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##### Register Old-Style (S3) Classes and Inheritance

Register an old-style (a.k.a. ‘S3’) class as a formally defined class. Simple usage will be of the form:

setOldClass(Classes)

where Classes is the character vector that would be the class attribute of the S3 object. Calls to setOldClass() in the code for a package allow the class to be used as a slot in formal (S4) classes and in signatures for methods (see Methods_for_S3). Formal classes can also contain a registered S3 class (see S3Part for details).

If the S3 class has a known set of attributes, an equivalent S4 class can be specified by S4Class= in the call to setOldClass(); see the section “Known Attributes”.

Keywords
programming , methods
##### Usage
setOldClass(Classes, prototype, where, test = FALSE, S4Class)
##### Arguments
Classes

A character vector, giving the names for S3 classes, as they would appear on the right side of an assignment of the class attribute in S3 computations.

In addition to S3 classes, an object type or other valid data part can be specified, if the S3 class is known to require its data to be of that form.

S4Class

optionally, the class definition or the class name of an S4 class. The new class will have all the slots and other properties of this class, plus any S3 inheritance implied by multiple names in the Classes argument. See the section on “S3 classes with known attributes” below.

prototype, where, test

These arguments are currently allowed, but not recommended in typical applications.

prototype: An optional object to use as the prototype. If the S3 class is not to be VIRTUAL (the default), the use of S4Class= is preferred.

where: Where to store the class definitions. Should be the default (the package namespace) for normal use in an application package.

test: flag, if TRUE, arrange to test inheritance explicitly for each object, needed if the S3 class can have a different set of class strings, with the same first string. Such classes are inherently malformed, are rare, and should be avoided.

##### Details

The name (or each of the names) in Classes will be defined as an S4 class, extending class oldClass, which is the ‘root’ of all old-style classes. S3 classes with multiple names in their class attribute will have a corresponding inheritance as formal classes. See the "mlm" example.

S3 classes have no formal definition, and therefore no formally defined slots. If no S4 class is supplied as a model, the class created will be a virtual class. If a virtual class (any virtual class) is used for a slot in another class, then the initializing method for the class needs to put something legal in that slot; otherwise it will be set to NULL.

See Methods_for_S3 for the details of method dispatch and inheritance with mixed S3 and S4 methods.

Some S3 classes cannot be represented as an ordinary combination of S4 classes and superclasses, because objects with the same initial string in the class attribute can have different strings following. Such classes are fortunately rare. They violate the basic idea of object-oriented programming and should be avoided. If you must deal with them, it is still possible to register such classes as S4 classes, but now the inheritance has to be verified for each object, and you must call setOldClass with argument test=TRUE.

##### Pre-Defined Old Classes

Many of the widely used S3 classes in the standard R distribution come pre-defined for use with S4. These don't need to be explicitly declared in your package (although it does no harm to do so).

The list .OldClassesList contains the old-style classes that are defined by the methods package. Each element of the list is a character vector, with multiple strings if inheritance is included. Each element of the list was passed to setOldClass when creating the methods package; therefore, these classes can be used in setMethod calls, with the inheritance as implied by the list.

##### S3 Classes with known attributes

A further specification of an S3 class can be made if the class is guaranteed to have some attributes of known class (where as with slots, “known” means that the attribute is an object of a specified class, or a subclass of that class).

In this case, the call to setOldClass() can supply an S4 class definition representing the known structure. Since S4 slots are implemented as attributes (largely for just this reason), the known attributes can be specified in the representation of the S4 class. The usual technique will be to create an S4 class with the desired structure, and then supply the class name or definition as the argument S4Class= to setOldClass().

See the definition of class "ts" in the examples below and the data.frame example in Section 10.2 of the reference. The call to setClass to create the S4 class can use the same class name, as here, so long as the call to setOldClass follows in the same package. For clarity it should be the next expression in the same file.

In the example, we define "ts" as a vector structure with a numeric slot for "tsp". The validity of this definition relies on an assertion that all the S3 code for this class is consistent with that definition; specifically, that all "ts" objects will behave as vector structures and will have a numeric "tsp" attribute. We believe this to be true of all the base code in R, but as always with S3 classes, no guarantee is possible.

The S4 class definition can have virtual superclasses (as in the "ts" case) if the S3 class is asserted to behave consistently with these (in the example, time-series objects are asserted to be consistent with the '>structure class).

Failures of the S3 class to live up to its asserted behavior will usually go uncorrected, since S3 classes inherently have no definition, and the resulting invalid S4 objects can cause all sorts of grief. Many S3 classes are not candidates for known slots, either because the presence or class of the attributes are not guaranteed (e.g., dimnames in arrays, although these are not even S3 classes), or because the class uses named components of a list rather than attributes (e.g., "lm"). An attribute that is sometimes missing cannot be represented as a slot, not even by pretending that it is present with class "NULL", because attributes, unlike slots, can not have value NULL.

One irregularity that is usually tolerated, however, is to optionally add other attributes to those guaranteed to exist (for example, "terms" in "data.frame" objects returned by model.frame). Validity checks by validObject ignore extra attributes; even if this check is tightened in the future, classes extending S3 classes would likely be exempted because extra attributes are so common.

##### References

Chambers, John M. (2016) Extending R, Chapman & Hall. (Chapters 9 and 10, particularly Section 10.8)

setClass, setMethod

##### Aliases
• setOldClass
• .setOldIs
• POSIXct-class
• POSIXlt-class
• POSIXt-class
• aov-class
• maov-class
• anova-class
• anova.glm-class
• anova.glm.null-class
• Date-class
• data.frame-class
• data.frameRowLabels-class
• density-class
• dump.frames-class
• factor-class
• formula-class
• glm-class
• glm.null-class
• hsearch-class
• integrate-class
• libraryIQR-class
• lm-class
• logLik-class
• mlm-class
• mtable-class
• mts-class
• ordered-class
• packageIQR-class
• packageInfo-class
• recordedplot-class
• rle-class
• socket-class
• summaryDefault-class
• summary.table-class
• oldClass-class
• .OldClassesList
• table-class
• initialize,data.frame-method
• initialize,factor-method
• initialize,ordered-method
• initialize,table-method
• initialize,summary.table-method
##### Examples
library(methods) # NOT RUN { require(stats) ## "lm" and "mlm" are predefined; if they were not this would do it: # } # NOT RUN { setOldClass(c("mlm", "lm")) # } # NOT RUN { ## Define a new generic function to compute the residual degrees of freedom setGeneric("dfResidual", function(model) stop(gettextf( "This function only works for fitted model objects, not class %s", class(model)))) setMethod("dfResidual", "lm", function(model)model\$df.residual) ## dfResidual will work on mlm objects as well as lm objects myData <- data.frame(time = 1:10, y = (1:10)^.5) myLm <- lm(cbind(y, y^3) ~ time, myData) # } # NOT RUN { ## two examples extending S3 class "lm": class "xlm" directly ## and "ylm" indirectly setClass("xlm", slots = c(eps = "numeric"), contains = "lm") setClass("ylm", slots = c(header = "character"), contains = "xlm") ym1 = new("ylm", myLm, header = "Example", eps = 0.) ## for more examples, see ?\link{S3Class}. # } # NOT RUN { # } # NOT RUN { ## The code in R that defines "ts" as an S4 class setClass("ts", contains = "structure", slots = c(tsp = "numeric"), prototype(NA, tsp = rep(1,3))) # prototype to be a legal S3 time-series ## and now registers it as an S3 class setOldClass("ts", S4Class = "ts", where = envir) # } # NOT RUN { # } 
Documentation reproduced from package methods, version 3.5.2, License: Part of R 3.5.2

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