Take a look at formatReal. scientific thinks 0.251 has 17 digits and 0.255 has 3. It really doesn't make any sense to ask for more precision than you have (.Machine$double.eps) and you do often get spurious errors if you attempt to do so. So 15 digits is normally safe, but no more.
Note that there are decimal -> binary -> decimal conversions and you can't say which one introduced the small changes.
I completely agree with you. My problem arise when I try to compute a correlation. One of the variable seems to have equal values but it does not. Hence, it has a very low variance and so when I try to compute the correlation with another variable, this correlation is very high. I wonder if it would not be good to introduce a tolerance threshold. Is it meaningful to produce correlation when a variance is very low ?
See the example below :
> essai=matrix(c(0.266,.234,.005,.481,.1,.009,.4,.155,.255,.2,.34,.43),4,3)
> essai2=sweep(essai,2,apply(essai,2,sum),"/")
> x=coef(lm(essai2~scale(runif(4))))
> x
[,1] [,2] [,3]
(Intercept) 0.25000000 0.2500000 0.25000000
scale(runif(4)) 0.05307906 0.1330111 0.06936634
> cor(x[1,],runif(3))
[1] 0.932772
> var(x)
[,1] [,2] [,3]
[1,] 0.01938893 0.011518783 0.01778528
[2,] 0.01151878 0.006843202 0.01056607
[3,] 0.01778528 0.010566067 0.01631426
> var(x[1,])
[1] 1.92593e-33Obviously, I can introduce this threshold, but I wonder if 15 digits is always a good limit to avoid this kind of problems
> cor(round(x[1,],15),runif(3)) [1] NA Warning message: The standard deviation is zero in: cor(x, y, na.method, method == "kendall")
Thanks a lot to all,
Stéphane DRAY
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