On 04/21/2017 05:19 AM, Paul Johnson wrote:
We all agree it is a problem with digital computing, not unique to R. I
don't think that is the right place to stop.
What to do? The round example arose in a real funded project where 2 R
programs differed in results and cause was that one person got 57 and
another got 58. The explanation was found, but its less clear how to
prevent similar in future. Guidelines, anyone?
Note that the error is amplified by the use of round() at the end of the
calculation. Whoever wrote that code should know that by doing this the
result will possibly differ by 1 across users. So, ideally, s/he should
document this in his/her function to minimize the "surprise effect" on
the user side.
Another option is to not round() the final result and leave that
responsibility to the end user. That's what I would do.
H.
So far, these are my guidelines.
1. Insert L on numbers to signal that you really mean INTEGER. In R,
forgetting the L in a single number will usually promote whole calculation
to floats.
2. S3 variables are called 'numeric' if they are integer or double storage.
So avoid "is.numeric" and prefer "is.double".
3. == is a total fail on floats
4. Run print with digits=20 so we can see the less rounded number. Perhaps
start sessions with "options(digits=20)"
5. all.equal does what it promises, but one must be cautious.
Are there math habits we should follow?
For example, Is it generally true in R that (100*x)/y is more accurate than
100*(x/y), if x > y? (If that is generally true, couldn't the R
interpreter do it for the user?)
I've seen this problem before. In later editions of the game theory program
Gambit, extraordinary effort was taken to keep values symbolically as
integers as long as possible. Avoid division until the last steps. Same in
Swarm simulations. Gary Polhill wrote an essay about the Ghost in the
Machine along those lines, showing accidents from trusting floats.
I wonder now if all uses of > or < with numeric variables are suspect.
Oh well. If everybody posts their advice, I will write a summary.
Paul Johnson
University of Kansas
On Apr 21, 2017 12:02 AM, "PIKAL Petr" <petr.pi...@precheza.cz> wrote:
Hi
The problem is that people using Excel or probably other such spreadsheets
do not encounter this behaviour as Excel silently rounds all your
calculations and makes approximate comparison without telling it does so.
Therefore most people usually do not have any knowledge of floating point
numbers representation.
Cheers
Petr
-----Original Message-----
From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Paul
Johnson
Sent: Thursday, April 20, 2017 11:56 PM
To: R-help <r-help@r-project.org>
Subject: [R] Interesting quirk with fractions and rounding
Hello, R friends
My student unearthed this quirk that might interest you.
I wondered if this might be a bug in the R interpreter. If not a bug, it
certainly stands as a good example of the dangers of floating point numbers
in computing.
What do you think?
100*(23/40)
[1] 57.5
(100*23)/40
[1] 57.5
round(100*(23/40))
[1] 57
round((100*23)/40)
[1] 58
The result in the 2 rounds should be the same, I think. Clearly some
digital number devil is at work. I *guess* that when you put in whole
numbers and group them like this (100*23), the interpreter does integer
math, but if you group (23/40), you force a fractional division and a
floating point number. The results from the first 2 calculations are not
actually 57.5, they just appear that way.
Before you close the books, look at this:
aa <- 100*(23/40)
bb <- (100*23)/40
all.equal(aa,bb)
[1] TRUE
round(aa)
[1] 57
round(bb)
[1] 58
I'm putting this one in my collection of "difficult to understand"
numerical calculations.
If you have seen this before, I'm sorry to waste your time.
pj
--
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