Converting package using S3 to S4 classes, is ther

Question

I have an R package which currently uses S3 class system, with two different classes and several methods for generic S3 functions like plot, logLik and update (for model formula updating). As my code has become more complex with all the validity checking and if/else structures due to to the fact that there's no inheritance or dispatching based on two arguments in S3, I have started to think of converting my package to S4. But then I started to read about the advantages and and disadvantages of S3 versus S4, and I'm not so sure anymore. I found R-bloggers blog post about efficiency issues in S3 vs S4, and as that was 5 years ago, I tested the same thing now:

library(microbenchmark)
setClass("MyClass", representation(x="numeric"))
microbenchmark(structure(list(x=rep(1, 10^7)), class="MyS3Class"),
               new("MyClass", x=rep(1, 10^7)) )
Unit: milliseconds
                                                   expr
 structure(list(x = rep(1, 10^7)), class = "MyS3Class")
                       new("MyClass", x = rep(1, 10^7))
       min       lq   median       uq      max neval
 148.75049 152.3811 155.2263 159.8090 323.5678   100
  75.15198 123.4804 129.6588 131.5031 241.8913   100

So in this simple example, S4 was actually bit faster. Then I read SO question about using S3 vs S4, which was quite much in favor of S3. Especially @joshua-ulrich 's answer made me doubt against S4, as it said that

any slot change requires a full object copy

That feels like a big issue if I consider my case where I'm updating my object in every iteration when optimizing log-likelihood of my model. After some googling I found John Chambers post about this issue, which seems to be changing in R 3.0.0.

So although I feel it would be beneficial to use S4 classes for some clarity in my codes (for example more classes inheriting from the main model class), and for the validity checks etc, I am now wondering is it worth all the work in terms of performance? So, performance wise, is there real performance differences between S3 and S4? Is there some other performance issues I should be considering? Or is it even possible to say something about this issue in general?

EDIT: As @DWin and @g-grothendieck suggested, the above benchmarking doesn't consider the case where the slot of an existing object is altered. So here's another benchmark which is more relevant to the true application (the functions in the example could be get/set functions for some elements in the model, which are altered when maximizing the log-likelihood):

objS3<-structure(list(x=rep(1, 10^3), z=matrix(0,10,10), y=matrix(0,10,10)),
                 class="MyS3Class")
fnS3<-function(obj,a){
  obj$y<-a
  obj
}

setClass("MyClass", representation(x="numeric",z="matrix",y="matrix"))
objS4<-new("MyClass", x=rep(1, 10^3),z=matrix(0,10,10),y=matrix(0,10,10))
fnS4<-function(obj,a){ 
  obj@y<-a
  obj
}

a<-matrix(1:100,10,10)
microbenchmark(fnS3(objS3,a),fnS4(objS4,a))
Unit: microseconds
           expr    min     lq median     uq    max neval
 fnS3(objS3, a)  6.531  7.464  7.932  9.331 26.591   100
 fnS4(objS4, a) 21.459 22.393 23.325 23.792 73.708   100

The benchmarks are performed on R 2.15.2, on 64bit Windows 7. So here S4 is clearly slower.

Answer 1

• First of all, you can easily have S3 methods for S4 classes:

  > extract <- function (x, ...) x@x
  > setGeneric ("extr4", def=function (x, ...){})
  [1] "extr4"
  > setMethod ("extr4", signature= "MyClass", definition=extract)
  [1] "extr4"
  > `[.MyClass` <- extract
  > `[.MyS3Class` <- function (x, ...) x$x
  > microbenchmark (objS3[], objS4 [], extr4 (objS4), extract (objS4))
  Unit: nanoseconds
             expr   min      lq  median      uq   max neval
          objS3[]  6775  7264.5  7578.5  8312.0 39531   100
          objS4[]  5797  6705.5  7124.0  7404.0 13550   100
     extr4(objS4) 20534 21512.0 22106.0 22664.5 54268   100
   extract(objS4)   908  1188.0  1328.0  1467.0 11804   100
  

---

edit: due to Hadley's comment, change the experiment to plot:

> `plot.MyClass` <- extract
> `plot.MyS3Class` <- function (x, ...) x$x
> microbenchmark (plot (objS3), plot (objS4), extr4 (objS4), extract (objS4))
Unit: nanoseconds
           expr   min      lq median      uq     max neval
    plot(objS3) 28915 30172.0  30591 30975.5 1887824   100
    plot(objS4) 25353 26121.0  26471 26960.0  411508   100
   extr4(objS4) 20395 21372.5  22001 22385.5   31359   100
 extract(objS4)   979  1328.0   1398  1677.0    3982   100

for an S4 method for plot I get:

    plot(objS4) 19835 20428.5 21336.5 22175.0 58876   100

So yes, [ has an exceptionally fast dispatch mechanism (which is good, because I think extraction and the corresponding replacement functions are among the most frequently called methods. But no, S4 dispatch isn't slower than S3 dispatch.

---

Here the S3 method on the S4 object is as fast as the S3 method on the S3 object. However, calling without dispatch is still faster.

• there are some things that work much better as S3 such as as.matrix or as.data.frame
  For some reason, defining these as S3 means that e.g. lm (formula, objS4) will work out of the box. This doesn't work with as.data.frame being defined as S4 method.

• Also it is much more convenient to call debug on a S3 method.

• some other things will not work with S3, e.g. dispatching on the second argument.

• Whether there will be any noticable drop in performance obviously depends on your class, that is, what kind of structures you have, how large the objects are and how often methods are called. A few μs of method dispatch won't matter with a calculation of ms or even s. But μs do matter when a function is called billions of times.

• One thing that caused noticable performance drop for some functions that are called often ([) is S4 validation (a fair number of checks done in validObject) - however, I'm glad to have it, so I use it.Internally I use workhorse functions that skip this step.

• In case you have large data and call-by-reference would help your performance, you may want to have a look at reference classes. I've never really worked with them so far, so I cannot comment on this.

Answer 2

If you are concerned about performance, benchmark it. If you really need multiple inheritance or multiple dispatch, use S4. Otherwise use S3.

Answer 3

(This is pretty close to the boundary of a "question likely to elicit opinion" but believe it is an important issue, one for which you have offered code and data and useful citations, and so I hope there are no votes to close.)

I admit that I have never really understood the S4 model of programming. However, what Chambers' post was saying is that @<-, i.e. slot assignment, was being re-implemented as a primitive rather than as a closure so that it would not require a complete copy of an object when one component was altered. So the earlier state of affairs will be altered in R 3.0.0 beta. On my machine (a 5 year-old MacPro running R 3.0.0 beta) the relative difference was even greater. However, I did not think that was necessarily a good test, since it was not altering an existing copy of a named object with multiple slots.

res <-microbenchmark(structure(list(x=rep(1, 10^7)), class="MyS3Class"),
                new("MyClass", x=rep(1, 10^7)) )
summary(res)[ ,"median"]
#[1] 145.0541 103.4064

I think you should go with S4 since your brain structure is more flexible than mine and there are a lot of very smart people, Douglas Bates and Martin Maechler to name two other than John Chambers, who have used S4 methods for packages that require heavy processing. The Matrix and lme4 package both use S4 methods for critical functions.