setMethod {methods} | R Documentation

## Create and Save a Method

### Description

Create a method for a generic function, corresponding to a signature of classes for the arguments. Standard usage will be of the form:

`setMethod(f, signature, definition)`

where `f` is the name of the function, `signature` specifies the argument classes for which the method applies and `definition` is the function definition for the method.

### Usage

    setMethod(f, signature=character(), definition,
              where = topenv(parent.frame()),
              valueClass = NULL, sealed = FALSE)

### Arguments

`f` | The character-string name of the generic function. The unquoted name usually works as well (evaluating to the generic function), except for a few functions in the base package.
`signature` | The classes required for some of the arguments. Most applications just require one or two character strings matching the first argument(s) in the signature. More complicated cases follow R's rule for argument matching. See the details below; however, if the signature is not trivial, you should use `method.skeleton` to generate a valid call to `setMethod`.
`definition` | A function definition, which will become the method called when the arguments in a call to `f` match the classes in `signature`, directly or through inheritance. The definition must be a function with the same formal arguments as the generic; however, `setMethod()` will handle methods that add arguments, if `...` is a formal argument to the generic. See the Details section.
`where, valueClass, sealed` | These arguments are allowed but either obsolete or rarely appropriate. `where`: where to store the definition; should be the default, the namespace for the package. `valueClass` Obsolete. `sealed` prevents the method being redefined, but should never be needed when the method is defined in the source code of a package.

### Value

The function exists for its side-effect. The definition will be stored in a special metadata object and incorporated in the generic function when the corresponding package is loaded into an R session.

### Method Selection: Avoiding Ambiguity

When defining methods, it's important to ensure that methods are selected correctly; in particular, packages should be designed to avoid ambiguous method selection.

To describe method selection, consider first the case where only one formal argument is in the active signature; that is, there is only one argument, `x` say, for which methods have been defined. The generic function has a table of methods, indexed by the class for the argument in the calls to `setMethod`. If there is a method in the table for the class of `x` in the call, this method is selected.

If not, the next best methods would correspond to the direct superclasses of `class(x)`—those appearing in the `contains=` argument when that class was defined. If there is no method for any of these, the next best would correspond to the direct superclasses of the first set of superclasses, and so on.

The first possible source of ambiguity arises if the class has several direct superclasses and methods have been defined for more than one of those; R will consider these equally valid and report an ambiguous choice. If your package has the class definition for `class(x)`, then you need to define a method explicitly for this combination of generic function and class.

When more than one formal argument appears in the method signature, R requires the “best” method to be chosen unambiguously for each argument. Ambiguities arise when one method is specific about one argument while another is specific about a different argument. A call that satisfies both requirements is then ambiguous: The two methods look equally valid, which should be chosen? In such cases the package needs to add a third method requiring both arguments to match.

The most common examples arise with binary operators. Methods may be defined for individual operators, for special groups of operators such as `Arith` or for group `Ops`.

### Exporting Methods

If a package defines methods for generic functions, those methods should be exported if any of the classes involved are exported; in other words, if someone using the package might expect these methods to be called. Methods are exported by including an `exportMethods()` directive in the `NAMESPACE` file for the package, with the arguments to the directive being the names of the generic functions for which methods have been defined.

Exporting methods is always desirable in the sense of declaring what you want to happen, in that you do expect users to find such methods. It can be essential in the case that the method was defined for a function that is not originally a generic function in its own package (for example, `plot()` in the `graphics` package). In this case it may be that the version of the function in the R session is not generic, and your methods will not be called.

Exporting methods for a function also exports the generic version of the function. Keep in mind that this does not conflict with the function as it was originally defined in another package; on the contrary, it's designed to ensure that the function in the R session dispatches methods correctly for your classes and continues to behave as expected when no specific methods apply. See Methods_Details for the actual mechanism.

### Details

The call to `setMethod` stores the supplied method definition in the metadata table for this generic function in the environment, typically the global environment or the namespace of a package. In the case of a package, the table object becomes part of the namespace or environment of the package. When the package is loaded into a later session, the methods will be merged into the table of methods in the corresponding generic function object.

Generic functions are referenced by the combination of the function name and the package name; for example, the function `"show"` from the package `"methods"`. Metadata for methods is identified by the two strings; in particular, the generic function object itself has slots containing its name and its package name. The package name of a generic is set according to the package from which it originally comes; in particular, and frequently, the package where a non-generic version of the function originated. For example, generic functions for all the functions in package base will have `"base"` as the package name, although none of them is an S4 generic on that package. These include most of the base functions that are primitives, rather than true functions; see the section on primitive functions in the documentation for `setGeneric` for details.

Multiple packages can have methods for the same generic function; that is, for the same combination of generic function name and package name. Even though the methods are stored in separate tables in separate environments, loading the corresponding packages adds the methods to the table in the generic function itself, for the duration of the session.

The class names in the signature can be any formal class, including basic classes such as `"numeric"`, `"character"`, and `"matrix"`. Two additional special class names can appear: `"ANY"`, meaning that this argument can have any class at all; and `"missing"`, meaning that this argument must not appear in the call in order to match this signature. Don't confuse these two: if an argument isn't mentioned in a signature, it corresponds implicitly to class `"ANY"`, not to `"missing"`. See the example below. Old-style (‘S3’) classes can also be used, if you need compatibility with these, but you should definitely declare these classes by calling `setOldClass` if you want S3-style inheritance to work.

Method definitions can have default expressions for arguments, but only if the generic function must have some default expression for the same argument. (This restriction is imposed by the way R manages formal arguments.) If so, and if the corresponding argument is missing in the call to the generic function, the default expression in the method is used. If the method definition has no default for the argument, then the expression supplied in the definition of the generic function itself is used, but note that this expression will be evaluated using the enclosing environment of the method, not of the generic function. Method selection does not evaluate default expressions. All actual (non-missing) arguments in the signature of the generic function will be evaluated when a method is selected—when the call to `standardGeneric(f)` occurs. Note that specifying class `"missing"` in the signature does not require any default expressions.

It is possible to have some differences between the formal arguments to a method supplied to `setMethod` and those of the generic. Roughly, if the generic has ... as one of its arguments, then the method may have extra formal arguments, which will be matched from the arguments matching ... in the call to `f`. (What actually happens is that a local function is created inside the method, with the modified formal arguments, and the method is re-defined to call that local function.)

Method dispatch tries to match the class of the actual arguments in a call to the available methods collected for `f`. If there is a method defined for the exact same classes as in this call, that method is used. Otherwise, all possible signatures are considered corresponding to the actual classes or to superclasses of the actual classes (including `"ANY"`). The method having the least distance from the actual classes is chosen; if more than one method has minimal distance, one is chosen (the lexicographically first in terms of superclasses) but a warning is issued. All inherited methods chosen are stored in another table, so that the inheritance calculations only need to be done once per session per sequence of actual classes. See Methods_Details and Section 10.7 of the reference for more details.

### References

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

### See Also

Methods_for_Nongenerics discusses method definition for functions that are not generic functions in their original package; Methods_for_S3 discusses the integration of formal methods with the older S3 methods.

`method.skeleton`, which is the recommended way to generate a skeleton of the call to `setMethod`, with the correct formal arguments and other details.

Methods_Details and the links there for a general discussion, `dotsMethods` for methods that dispatch on “...”, and `setGeneric` for generic functions.

### Examples

    ## examples for a simple class with two numeric slots.
    ## (Run example(setMethod) to see the class and function definitions)

    ## methods for plotting track objects
    ##
    ## First, with only one object as argument, plot the two slots
    ##  y must be included in the signature, it would default to "ANY"
    setMethod("plot", signature(x="track", y="missing"),
      function(x,  y, ...) plot(x@x, x@y, ...)
    )

    ## plot numeric data on either axis against a track object
    ## (reducing the track object to the cumulative distance along the track)
    ## Using a short form for the signature, which matches like formal arguments
    setMethod("plot", c("track", "numeric"),
     function(x, y, ...) plot(cumdist(x@x, x@y), y,  xlab = "Distance",...)
    )

    ## and similarly for the other axis
    setMethod("plot", c("numeric", "track"),
     function(x, y, ...) plot(x, cumdist(y@x, y@y),  ylab = "Distance",...)
    )

    t1 <- new("track", x=1:20, y=(1:20)^2)
    plot(t1)
    plot(qnorm(ppoints(20)), t1)

    ## Now a class that inherits from "track", with a vector for data at
    ## the points
      setClass("trackData", contains = c("numeric", "track"))

    tc1 <- new("trackData", t1, rnorm(20))

    ## a method for plotting the object
    ## This method has an extra argument, allowed because ... is an
    ## argument to the generic function.
    setMethod("plot", c("trackData", "missing"),
    function(x, y, maxRadius = max(par("cin")), ...) {
      plot(x@x, x@y, type = "n", ...)
      symbols(x@x, x@y, circles = abs(x), inches = maxRadius)
      }
    )
    plot(tc1)

    ## Without other methods for "trackData", methods for "track"
    ## will be selected by inheritance

    plot(qnorm(ppoints(20)), tc1)

    ## defining methods for primitive function.
    ## Although "[" and "length" are not ordinary functions
    ## methods can be defined for them.
    setMethod("[", "track",
      function(x, i, j, ..., drop) {
        x@x <- x@x[i]; x@y <- x@y[i]
        x
      })
    plot(t1[1:15])

    setMethod("length", "track", function(x)length(x@y))
    length(t1)

    ## Methods for binary operators
    ## A method for the group generic "Ops" will apply to all operators
    ## unless a method for a more specific operator has been defined.

    ## For one trackData argument, go on with just the data part
    setMethod("Ops", signature(e1 = "trackData"),
        function(e1, e2) callGeneric(e1@.Data, e2))

    setMethod("Ops", signature(e2 = "trackData"),
        function(e1, e2) callGeneric(e1, e2@.Data))

    ## At this point, the choice of a method for a call with BOTH
    ## arguments from "trackData" is ambiguous.  We must define a method.

    setMethod("Ops", signature(e1 = "trackData", e2 = "trackData"),
        function(e1, e2) callGeneric(e1@.Data, e2@.Data))
    ## (well, really we should only do this if the "track" part
    ## of the two arguments matched)

    tc1 +1

    1/tc1

    all(tc1 == tc1)

[Package methods version 3.4.2 Index]