Methods {methods} | R Documentation

## General Information on Methods

### Description

This documentation section covers some general topics on how methods work and how the methods package interacts with the rest of R. The information is usually not needed to get started with methods and classes, but may be helpful for moderately ambitious projects, or when something doesn't work as expected.

The section “How Methods Work” describes the underlying mechanism; “S3 Methods and Generic Functions” gives the rules applied when S4 classes and methods interact with older S3 methods; “Method Selection and Dispatch” provides more details on how class definitions determine which methods are used; “Generic Functions” discusses generic functions as objects. For additional information specifically about class definitions, see `Classes`.

### How Methods Work

A generic function has associated with it a collection of other functions (the methods), all of which have the same formal arguments as the generic. See the “Generic Functions” section below for more on generic functions themselves.

Each R package will include methods metadata objects corresponding to each generic function for which methods have been defined in that package. When the package is loaded into an R session, the methods for each generic function are cached, that is, stored in the environment of the generic function along with the methods from previously loaded packages. This merged table of methods is used to dispatch or select methods from the generic, using class inheritance and possibly group generic functions (see `GroupGenericFunctions`) to find an applicable method. See the “Method Selection and Dispatch” section below. The caching computations ensure that only one version of each generic function is visible globally; although different attached packages may contain a copy of the generic function, these behave identically with respect to method selection. In contrast, it is possible for the same function name to refer to more than one generic function, when these have different `package` slots. In the latter case, R considers the functions unrelated: A generic function is defined by the combination of name and package. See the “Generic Functions” section below.

The methods for a generic are stored according to the corresponding `signature` in the call to `setMethod` that defined the method. The signature associates one class name with each of a subset of the formal arguments to the generic function. Which formal arguments are available, and the order in which they appear, are determined by the `"signature"` slot of the generic function itself. By default, the signature of the generic consists of all the formal arguments except ..., in the order they appear in the function definition.

Trailing arguments in the signature of the generic will be inactive if no method has yet been specified that included those arguments in its signature. Inactive arguments are not needed or used in labeling the cached methods. (The distinction does not change which methods are dispatched, but ignoring inactive arguments improves the efficiency of dispatch.)

All arguments in the signature of the generic function will be evaluated when the function is called, rather than using the traditional lazy evaluation rules of S. Therefore, it's important to exclude from the signature any arguments that need to be dealt with symbolically (such as the first argument to function `substitute`). Note that only actual arguments are evaluated, not default expressions. A missing argument enters into the method selection as class `"missing"`.

The cached methods are stored in an environment object. The names used for assignment are a concatenation of the class names for the active arguments in the method signature.

### Methods for S3 Generic Functions

S4 methods may be wanted for functions that also have S3 methods, corresponding to classes for the first formal argument of an S3 generic function–either a regular R function in which there is a call to the S3 dispatch function, `UseMethod`, or one of a fixed set of primitive functions, which are not true functions but go directly to C code. In either case S3 method dispatch looks at the class of the first argument or the class of either argument in a call to one of the primitive binary operators. S3 methods are ordinary functions with the same arguments as the generic function (for primitives the formal arguments are not actually part of the object, but are simulated when the object is printed or viewed by `args()`). The “signature” of an S3 method is identified by the name to which the method is assigned, composed of the name of the generic function, followed by `"."`, followed by the name of the class. For details, see S3Methods.

To implement a method for one of these functions corresponding to S4 classes, there are two possibilities: either an S4 method or an S3 method with the S4 class name. The S3 method is only possible if the intended signature has the first argument and nothing else. In this case, the recommended approach is to define the S3 method and also supply the identical function as the definition of the S4 method. If the S3 generic function was `f3(x, ...)` and the S4 class for the new method was `"myClass"`:

`f3.myClass <- function(x, ...) { ..... }`

`setMethod("f3", "myClass", f3.myClass)`

The reasons for defining both S3 and S4 methods are as follows:

1. An S4 method alone will not be seen if the S3 generic function is called directly. However, primitive functions and operators are exceptions: The internal C code will look for S4 methods if and only if the object is an S4 object. In the examples, the method for ``[`` for class `"myFrame"` will always be called for objects of this class.

   For the same reason, an S4 method defined for an S3 class will not be called from internal code for a non-S4 object. (See the example for function `Math` and class `"data.frame"` in the examples.)

2. An S3 method alone will not be called if there is any eligible non-default S4 method. (See the example for function `f3` and class `"classA"` in the examples.)

Details of the selection computations are given below.

When an S4 method is defined for an existing function that is not an S4 generic function (whether or not the existing function is an S3 generic), an S4 generic function will be created corresponding to the existing function and the package in which it is found (more precisely, according to the implicit generic function either specified or inferred from the ordinary function; see `implicitGeneric`). A message is printed after the initial call to `setMethod`; this is not an error, just a reminder that you have created the generic. Creating the generic explicitly by the call

`setGeneric("f3")`

avoids the message, but has the same effect. The existing function becomes the default method for the S4 generic function. Primitive functions work the same way, but the S4 generic function is not explicitly created (as discussed below).

S4 and S3 method selection are designed to follow compatible rules of inheritance, as far as possible. S3 classes can be used for any S4 method selection, provided that the S3 classes have been registered by a call to `setOldClass`, with that call specifying the correct S3 inheritance pattern. S4 classes can be used for any S3 method selection; when an S4 object is detected, S3 method selection uses the contents of `extends(class(x))` as the equivalent of the S3 inheritance (the inheritance is cached after the first call).

An existing S3 method may not behave as desired for an S4 subclass, in which case utilities such as `asS3` and `S3Part` may be useful. If the S3 method fails on the S4 object, `asS3(x)` may be passed instead; if the object returned by the S3 method needs to be incorporated in the S4 object, the replacement function for `S3Part` may be useful, as in the method for class `"myFrame"` in the examples.

Here are details explaining the reasons for defining both S3 and S4 methods. Calls still accessing the S3 generic function directly will not see S4 methods, except in the case of primitive functions. This means that calls to the generic function from namespaces that import the S3 generic but not the S4 version will only see S3 methods. On the other hand, S3 methods will only be selected from the S4 generic function as part of its default (`"ANY"`) method. If there are inherited S4 non-default methods, these will be chosen in preference to any S3 method.

S3 generic functions implemented as primitive functions (including binary operators) are an exception to recognizing only S3 methods. These functions dispatch both S4 and S3 methods from the internal C code. There is no explicit generic function, either S3 or S4. The internal code looks for S4 methods if the first argument, or either of the arguments in the case of a binary operator, is an S4 object. If no S4 method is found, a search is made for an S3 method.

S4 methods can be defined for an S3 generic function and an S3 class, but if the function is a primitive, such methods will not be selected if the object in question is not an S4 object. In the examples below, for instance, an S4 method for signature `"data.frame"` for function `f3()` would be called for the S3 object `df1`. A similar S4 method for primitive function ``[`` would be ignored for that object, but would be called for the S4 object `mydf1` that inherits from `"data.frame"`. Defining both an S3 and S4 method removes this inconsistency.

### Method Selection and Dispatch: Details

When a call to a generic function is evaluated, a method is selected corresponding to the classes of the actual arguments in the signature. First, the cached methods table is searched for an exact match; that is, a method stored under the signature defined by the string value of `class(x)` for each non-missing argument, and `"missing"` for each missing argument. If no method is found directly for the actual arguments in a call to a generic function, an attempt is made to match the available methods to the arguments by using the superclass information about the actual classes.

Each class definition may include a list of one or more superclasses of the new class. The simplest and most common specification is by the `contains=` argument in the call to `setClass`. Each class named in this argument is a superclass of the new class. Two additional mechanisms for defining superclasses exist. A call to `setClassUnion` creates a union class that is a superclass of each of the members of the union. A call to `setIs` can create an inheritance relationship that is not the simple one of containing the superclass representation in the new class. Arguments `coerce` and `replace` supply methods to convert to the superclass and to replace the part corresponding to the superclass. (In addition, a `test=` argument allows conditional inheritance; conditional inheritance is not recommended and is not used in method selection.) All three mechanisms are treated equivalently for purposes of method selection: they define the direct superclasses of a particular class. For more details on the mechanisms, see `Classes`.

The direct superclasses themselves may have superclasses, defined by any of the same mechanisms, and similarly through further generations. Putting all this information together produces the full list of superclasses for this class. The superclass list is included in the definition of the class that is cached during the R session. Each element of the list describes the nature of the relationship (see `SClassExtension` for details). Included in the element is a `distance` slot containing the path length for the relationship: `1` for direct superclasses (regardless of which mechanism defined them), then `2` for the direct superclasses of those classes, and so on. In addition, any class implicitly has class `"ANY"` as a superclass. The distance to `"ANY"` is treated as larger than the distance to any actual class. The special class `"missing"` corresponding to missing arguments has only `"ANY"` as a superclass, while `"ANY"` has no superclasses.

When a class definition is created or modified, the superclasses are ordered, first by a stable sort of the all superclasses by distance. If the set of superclasses has duplicates (that is, if some class is inherited through more than one relationship), these are removed, if possible, so that the list of superclasses is consistent with the superclasses of all direct superclasses. See the reference on inheritance for details.

The information about superclasses is summarized when a class definition is printed.

When a method is to be selected by inheritance, a search is made in the table for all methods directly corresponding to a combination of either the direct class or one of its superclasses, for each argument in the active signature. For an example, suppose there is only one argument in the signature and that the class of the corresponding object was `"dgeMatrix"` (from the recommended package `Matrix`). This class has two direct superclasses and through these 4 additional superclasses. Method selection finds all the methods in the table of directly specified methods labeled by one of these classes, or by `"ANY"`.

When there are multiple arguments in the signature, each argument will generate a similar list of inherited classes. The possible matches are now all the combinations of classes from each argument (think of the function `outer` generating an array of all possible combinations). The search now finds all the methods matching any of this combination of classes. For each argument, the position in the list of superclasses of that argument's class defines which method or methods (if the same class appears more than once) match best. When there is only one argument, the best match is unambiguous. With more than one argument, there may be zero or one match that is among the best matches for all arguments.

If there is no best match, the selection is ambiguous and a message is printed noting which method was selected (the first method lexicographically in the ordering) and what other methods could have been selected. Since the ambiguity is usually nothing the end user could control, this is not a warning. Package authors should examine their package for possible ambiguous inheritance by calling `testInheritedMethods`.

When the inherited method has been selected, the selection is cached in the generic function so that future calls with the same class will not require repeating the search. Cached inherited selections are not themselves used in future inheritance searches, since that could result in invalid selections. If you want inheritance computations to be done again (for example, because a newly loaded package has a more direct method than one that has already been used in this session), call `resetGeneric`. Because classes and methods involving them tend to come from the same package, the current implementation does not reset all generics every time a new package is loaded.

Besides being initiated through calls to the generic function, method selection can be done explicitly by calling the function `selectMethod`.

Once a method has been selected, the evaluator creates a new context in which a call to the method is evaluated. The context is initialized with the arguments from the call to the generic function. These arguments are not rematched. All the arguments in the signature of the generic will have been evaluated (including any that are currently inactive); arguments that are not in the signature will obey the usual lazy evaluation rules of the language. If an argument was missing in the call, its default expression if any will not have been evaluated, since method dispatch always uses class `missing` for such arguments.

A call to a generic function therefore has two contexts: one for the function and a second for the method. The argument objects will be copied to the second context, but not any local objects created in a nonstandard generic function. The other important distinction is that the parent (“enclosing”) environment of the second context is the environment of the method as a function, so that all R programming techniques using such environments apply to method definitions as ordinary functions.

For further discussion of method selection and dispatch, see the first reference.

### Generic Functions

In principle, a generic function could be any function that evaluates a call to `standardGeneric()`, the internal function that selects a method and evaluates a call to the selected method. In practice, generic functions are special objects that in addition to being from a subclass of class `"function"` also extend the class `genericFunction`. Such objects have slots to define information needed to deal with their methods. They also have specialized environments, containing the tables used in method selection.

The slots `"generic"` and `"package"` in the object are the character string names of the generic function itself and of the package from which the function is defined. As with classes, generic functions are uniquely defined in R by the combination of the two names. There can be generic functions of the same name associated with different packages (although inevitably keeping such functions cleanly distinguished is not always easy). On the other hand, R will enforce that only one definition of a generic function can be associated with a particular combination of function and package name, in the current session or other active version of R.

Tables of methods for a particular generic function, in this sense, will often be spread over several other packages. The total set of methods for a given generic function may change during a session, as additional packages are loaded. Each table must be consistent in the signature assumed for the generic function.

R distinguishes standard and nonstandard generic functions, with the former having a function body that does nothing but dispatch a method. For the most part, the distinction is just one of simplicity: knowing that a generic function only dispatches a method call allows some efficiencies and also removes some uncertainties.

In most cases, the generic function is the visible function corresponding to that name, in the corresponding package. There are two exceptions, implicit generic functions and the special computations required to deal with R's primitive functions. Packages can contain a table of implicit generic versions of functions in the package, if the package wishes to leave a function non-generic but to constrain what the function would be like if it were generic. Such implicit generic functions are created during the installation of the package, essentially by defining the generic function and possibly methods for it, and then reverting the function to its non-generic form. (See implicitGeneric for how this is done.) The mechanism is mainly used for functions in the older packages in R, which may prefer to ignore S4 methods. Even in this case, the actual mechanism is only needed if something special has to be specified. All functions have a corresponding implicit generic version defined automatically (an implicit, implicit generic function one might say). This function is a standard generic with the same arguments as the non-generic function, with the non-generic version as the default (and only) method, and with the generic signature being all the formal arguments except ....

The implicit generic mechanism is needed only to override some aspect of the default definition. One reason to do so would be to remove some arguments from the signature. Arguments that may need to be interpreted literally, or for which the lazy evaluation mechanism of the language is needed, must not be included in the signature of the generic function, since all arguments in the signature will be evaluated in order to select a method. For example, the argument `expr` to the function `with` is treated literally and must therefore be excluded from the signature.

One would also need to define an implicit generic if the existing non-generic function were not suitable as the default method. Perhaps the function only applies to some classes of objects, and the package designer prefers to have no general default method. In the other direction, the package designer might have some ideas about suitable methods for some classes, if the function were generic. With reasonably modern packages, the simple approach in all these cases is just to define the function as a generic. The implicit generic mechanism is mainly attractive for older packages that do not want to require the methods package to be available.

Generic functions will also be defined but not obviously visible for functions implemented as primitive functions in the base package. Primitive functions look like ordinary functions when printed but are in fact not function objects but objects of two types interpreted by the R evaluator to call underlying C code directly. Since their entire justification is efficiency, R refuses to hide primitives behind a generic function object. Methods may be defined for most primitives, and corresponding metadata objects will be created to store them. Calls to the primitive still go directly to the C code, which will sometimes check for applicable methods. The definition of “sometimes” is that methods must have been detected for the function in some package loaded in the session and `isS4(x)` is `TRUE` for the first argument (or for the second argument, in the case of binary operators). You can test whether methods have been detected by calling `isGeneric` for the relevant function and you can examine the generic function by calling `getGeneric`, whether or not methods have been detected. For more on generic functions, see the first reference and also section 2 of R Internals.

### Method Definitions

All method definitions are stored as objects from the `MethodDefinition` class. Like the class of generic functions, this class extends ordinary R functions with some additional slots: `"generic"`, containing the name and package of the generic function, and two signature slots, `"defined"` and `"target"`, the first being the signature supplied when the method was defined by a call to `setMethod`. The `"target"` slot starts off equal to the `"defined"` slot. When an inherited method is cached after being selected, as described above, a copy is made with the appropriate `"target"` signature. Output from `showMethods`, for example, includes both signatures.

Method definitions are required to have the same formal arguments as the generic function, since the method dispatch mechanism does not rematch arguments, for reasons of both efficiency and consistency.

### References

Chambers, John M. (2008) Software for Data Analysis: Programming with R Springer. (For the R version: see section 10.6 for method selection and section 10.5 for generic functions).

Chambers, John M.(2009) Developments in Class Inheritance and Method Selection https://statweb.stanford.edu/~jmc4/classInheritance.pdf.

Chambers, John M. (1998) Programming with Data Springer (For the original S4 version.)

### See Also

For more specific information, see `setGeneric`, `setMethod`, and `setClass`.

For the use of ... in methods, see dotsMethods.

### Examples

    ## A class that extends a registered S3 class inherits that class' S3
    ## methods.

    setClass("myFrame", contains = "data.frame",
        representation(timestamps = "POSIXt"))

    df1 <- data.frame(x = 1:10, y = rnorm(10), z = sample(letters,10))

    mydf1 <- new("myFrame", df1, timestamps = Sys.time())

    ## "myFrame" objects inherit "data.frame" S3 methods; e.g., for `[`

    mydf1[1:2, ] # a data frame object (with extra attributes)

    ## a method explicitly for "myFrame" class

    setMethod("[",
        signature(x = "myFrame"),
        function (x, i, j, ..., drop = TRUE)
        {
            S3Part(x) <- callNextMethod()
            x@timestamps <- c(Sys.time(), as.POSIXct(x@timestamps))
            x
        }
    )

    mydf1[1:2, ]

    setClass("myDateTime", contains = "POSIXt")

    now <- Sys.time() # class(now) is c("POSIXct", "POSIXt")
    nowLt <- as.POSIXlt(now)# class(nowLt) is c("POSIXlt", "POSIXt")

    mCt <- new("myDateTime", now)
    mLt <- new("myDateTime", nowLt)

    ## S3 methods for an S4 object will be selected using S4 inheritance
    ## Objects mCt and mLt have different S3Class() values, but this is
    ## not used.
    f3 <- function(x)UseMethod("f3") # an S3 generic to illustrate inheritance

    f3.POSIXct <- function(x) "The POSIXct result"
    f3.POSIXlt <- function(x) "The POSIXlt result"
    f3.POSIXt <- function(x) "The POSIXt result"

    stopifnot(identical(f3(mCt), f3.POSIXt(mCt)))
    stopifnot(identical(f3(mLt), f3.POSIXt(mLt)))

    ## An S4 object selects S3 methods according to its S4 "inheritance"

    setClass("classA", contains = "numeric",
             representation(realData = "numeric"))

    Math.classA <- function(x) {(getFunction(.Generic))(x@realData)}
    setMethod("Math", "classA", Math.classA)

    x <- new("classA", log(1:10), realData = 1:10)

    stopifnot(identical(abs(x), 1:10))

    setClass("classB", contains = "classA")

    y <- new("classB", x)

    stopifnot(identical(abs(y), abs(x))) # (version 2.9.0 or earlier fails here)

    ## an S3 generic: just for demonstration purposes
    f3 <- function(x, ...) UseMethod("f3")

    f3.default <- function(x, ...) "Default f3"

    ## S3 method (only) for classA
    f3.classA <- function(x, ...) "Class classA for f3"

    ## S3 and S4 method for numeric
    f3.numeric <- function(x, ...) "Class numeric for f3"
    setMethod("f3", "numeric", f3.numeric)

    ## The S3 method for classA and the closest inherited S3 method for classB
    ## are not found.

    f3(x); f3(y) # both choose "numeric" method

    ## to obtain the natural inheritance, set identical S3 and S4 methods
    setMethod("f3", "classA", f3.classA)

    f3(x); f3(y) # now both choose "classA" method

    ## Need to define an S3 as well as S4 method to use on an S3 object
    ## or if called from a package without the S4 generic

    MathFun <- function(x) { # a smarter "data.frame" method for Math group
      for (i in seq(length = ncol(x))[sapply(x, is.numeric)])
        x[, i] <- (getFunction(.Generic))(x[, i])
      x
    }
    setMethod("Math", "data.frame", MathFun)

    ## S4 method works for an S4 class containing data.frame,
    ## but not for data.frame objects (not S4 objects)

    try(logIris <- log(iris)) #gets an error from the old method

    ## Define an S3 method with the same computation

    Math.data.frame <- MathFun

    logIris <- log(iris)

[Package methods version 3.3.0 Index]