Hear, hear ! S-plus could have been a Microsoft product. It's
bloated and slow and it's everywhere.
Of course C does not stand for "computer".
The naming chain is CPL => BCPL => B => C. See Steve
Summit, C Programming FAQ's, number 20.38.
At 12:59 AM +0000 2/23/00, Radford Neal wrote:
>In article <[EMAIL PROTECTED]>,
>Frank E Harrell Jr <[EMAIL PROTECTED]> wrote:
>
> > S-Plus is a powerful object-oriented language based on the S
> > language (created at the same place that created the C language -
> > ATT Bell Labs - C is for "computer" S for "statistics").
>
>Usually, I find this sort of cheerleading just a bit annoying, but
>when given in response to a student's request for information about
>what to learn, it gets downright misleading.
>
>The truth: S-Plus is not particularly powerful, and it's not
>particularly well designed. Its implementation by the people at AT&T
>Bell labs is downright incompetent. Of course, it has lots of support
>for statistical methods, making it useful for statisticians to know,
>but it's a shame that this has had the effect of locking academic
>statisticians into this indifferently-designed and badly-implemented
>language, which is completely unusable some purposes, such as Markov
>chain Monte Carlo computations. The free "R" look-alike for S may fix
>at least the badly-implemented part of this dilema.
>
>Below is a posting I recently made to sci.stat.math, remarking on the
>amazingly *worse* performance of the latest release of S-Plus - which
>is up to SEVENTEEN times slower than the previous release.
>
>Finally, I doubt very much that the "C" language stands for "computer".
>What would it's predecessor language, called "B", have stood for?
>
> Radford Neal
>
>-------------------------------------------------------------------------
>
>In article <[EMAIL PROTECTED]>,
>Thomas Gatliffe <[EMAIL PROTECTED]> wrote:
>
> >R is a substitute but if you are really looking for the power of S-Plus you
> >won't be satisfied.
>
>This depends on what you mean by "power". S-Plus has some facilities
>that aren't in R, and some of the ones that R does have seem to be a
>bit less mature - eg, I couldn't off hand figure out how to get R's lm
>function to accept a "tolerance" parameter to allow it to accept
>almost singular (but still doable) problems. One the other hand, if
>by "power" you mean the ability to do anything involving substantial
>computation that isn't done by one of the built-in funcitons written
>in C, then S-Plus is about the worst language you could possibly choose,
>whereas R looks like it would be viable (though not especially fast).
>
>What is most incredible is that the inefficiency of S-Plus has gotten
>substantially worse in the latest release. It's so bad that I wonder
>if there is something peculiar about the version for our machine (SGI).
>Here are the times (in seconds) to run five simple test programs that
>are listed below:
>
> Program: test1 test2 test3 test4 test5
> Iterations: 500 1000 10000 1000 100000
>
> S-PLUS 3.4: 18.3 9.0 38.4 31.0 232.9
> S-PLUS 5.1: 75.0 152.6 317.1 544.8 224.1
> R 0.90.1: 56.5 5.7 47.0 18.1 27.9
>
>All results are on an SGI system with a 194 MHz R10000 processor.
>
>Looking first at the OLD version of S-Plus (3.4) versus R, one sees
>that on the first test, involving the built-in lm function, R is about
>three times slower, perhaps due to the quality of the implementation
>of that particular function. On the other tests, R is about the same
>speed as the old S-Plus, EXCEPT for test5, where the old S-Plus is
>about 10 times slower. This test appears to activate the S-Plus
>design flaw that causes some memory not to be recovered in loops.
>Execution then gets slower and slower as the loop goes round (it takes
>the old S-Plus only 5.3 seconds to do 10000 iterations, which would
>lead you to think it would take 53 seconds, not 232, to do 100000).
>Perusal of this test (below) will show you that it's not doing
>anything that you might not want to do in lots of your programs.
>Trying to avoid anything that might activate this bug while writing
>programs is a ridiculous waste of your time.
>
>Turning now to the NEW S-Plus (5.1), one sees that the test involving
>the built-in lm function is now about four times slower. Not too
>impressive an "upgrade". But that's nothing compared to tests 2 to 4,
>however - they're up to 17 times slower! And the last test shows that
>they still haven't fixed the memory bug.
>
>This new release is so ridiculously bad that I wonder whether there's
>something wrong with just the version for SGI machines. I'd be
>interested to find out what the results are on other machines. Anyone
>else with both the old and the new S-Plus want to try?
>
>For comparison, the Maple equivalent of test5 (see below) takes 3.5
>seconds to run on the same SGI machine as was used for the above
>tests. Clearly, R is not a particularly efficient interpreter either.
>R does seem to avoid any truly spectacular inefficiencies, however.
>
>Here are the programs, which don't do anything interesting or sensible
>- I just typed in various things somewhat arbitrarily - but do do the
>sort of things that one will want to do now and then. They were run
>for the numbers of iterations (n) shown above. Times were from the
>Unix "time" command, applied to an interactive run in which the
>function was read with "source", then run, after which q() was
>immediately done. For R, the workspace was saved at the end to match
>the behaviour of S in saving variables automatically.
>
>test1:
>
>time.test1 <- function (n)
>{
> x1 <- c(1,4,1,3,2,3,4)
> x2 <- c(5,0,2,3,5,2,1)
> y <- c(4,3,2,4,3,2,1)
>
> for (i in 1:n)
> { ys <- sample(y)
> m <- lm(ys~x1+x2)
> }
>
> invisible()
>}
>
>test2:
>
>time.test2 <- function (n)
>{
> x1 <- c(1,4,1,3,2,3,4)
> x2 <- c(5,0,2,3,5,2,1)
> y <- c(4,3,2,4,3,2,1)
>
> k <- 0
>
> for (i in 1:n)
> { ys <- sample(y)
> if (cor(ys,x1)>cor(ys,x2))
> { k <- k+1
> }
> }
>
> invisible()
>}
>
>test3:
>
>time.test3 <- function (n)
>{
> x1 <- c(1,4,1,3,2,3,4)
> x2 <- c(5,0,2,3,5,2,1)
> y <- c(4,3,2,4,3,2,1)
>
> k <- 0
>
> for (i in 1:n)
> { for (j in 1:length(y))
> { y[j] <- x1[j] + x2[j]*y[j]
> if (y[j]<10 || y[j]>10)
> { y[j] <- 2.3
> }
> }
> }
>
> invisible()
>}
>
>test4:
>
>time.test4 <- function (n)
>{
> x1 <- c(1,4,1,3,2,3,4)
> x2 <- c(5,0,2,3,5,2,1)
> y <- c(0,0,0,0,0,0,0)
>
> k <- 0
>
> for (i in 1:n)
> { for (j in 1:length(y))
> { y[j] <- cor(x1+x2,x1-x2*j)
> }
> for (j in 1:length(x1))
> { x1[j] <- 0.1 - x1[j]
> }
> }
>
> invisible()
>}
>
>test5:
>
>time.test5 <- function (n)
>{
> k <- 0
> for (j in 1:n)
> { v <- c(1,2,j,j,3)
> if (v[2]==2)
> { k <- k+1
> }
> }
>
> invisible()
>}
>
>Here's the Maple equivalent of test5:
>
>test := proc(n)
> local j,k,v;
> k := 0;
> for j to n do
> v := [1,2,j,j,3];
> if v[2] = 2 then
> k := k+1
> fi
> od;
>end;
>
>
>----------------------------------------------------------------------------
>Radford M. Neal [EMAIL PROTECTED]
>Dept. of Statistics and Dept. of Computer Science [EMAIL PROTECTED]
>University of Toronto http://www.cs.utoronto.ca/~radford
>----------------------------------------------------------------------------
>
>
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===
Jan de Leeuw; Professor and Chair, UCLA Department of Statistics;
US mail: 8142 Math Sciences Bldg, Box 951554, Los Angeles, CA 90095-1554
phone (310)-825-9550; fax (310)-206-5658; email: [EMAIL PROTECTED]
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