rtfm: help('qnorm') identifies arguments you overlooked. 1-x generates
roundoff error. Try the following:
x <- 10^(-(1:10))
qx <- qnorm(x)
q1x <- qnorm(1-x)
qlx <- qnorm(x, lower=FALSE)
> cbind(x, qx, q1x, qlx, qx.1x=qx+q1x, qx.lx=qx+qlx)
x qx q1x qlx qx.1x qx.lx
[1,] 1e-01 -1.281552 1.281552 1.281552 0.000000e+00 0
[2,] 1e-02 -2.326348 2.326348 2.326348 0.000000e+00 0
[3,] 1e-03 -3.090232 3.090232 3.090232 0.000000e+00 0
[4,] 1e-04 -3.719016 3.719016 3.719016 2.842171e-14 0
[5,] 1e-05 -4.264891 4.264891 4.264891 1.014300e-12 0
[6,] 1e-06 -4.753424 4.753424 4.753424 -5.809575e-12 0
[7,] 1e-07 -5.199338 5.199338 5.199338 9.784440e-11 0
[8,] 1e-08 -5.612001 5.612001 5.612001 -8.692842e-10 0
[9,] 1e-09 -5.997807 5.997807 5.997807 4.593952e-09 0
[10,] 1e-10 -6.361341 6.361341 6.361341 -1.270663e-08 0
hope this helps. Spencer Graves
On 2017-04-16 6:30 AM, Sheldon Maze wrote:
Hello All
I am looking for high precision values for the normal distribution in the
tail,(1e-10 and 1 - 1e-10) as the R package that I am using sets any number
which is out of this range to these values and then calls the qnorm and qt
function.
What I have noticed is that the qnorm implementation in R is not symmetric
when looking at the tails. This is quite surprising to me, as it is well
known that this distribution is symmetric, and I have seen implementations
in other languages that are symmetric. I have checked the qt function and
it is also not symmetric in the tails.
Here are the results from the qnorm function:
x qnorm(x) qnorm(1-x) qnorm(1-x) +
qnorm(x)
1e-2 -2.3263478740408408 2.3263478740408408 0.0 (i.e < machine
epsilon)
1e-3 -3.0902323061678132 3.0902323061678132 0.0 (i.e < machine
epsilon)
1e-4 -3.71901648545568 3.7190164854557084
2.8421709430404007e-14
1e-5 -4.2648907939228256 4.2648907939238399
1.014299755297543e-12
1e-10 -6.3613409024040557 6.3613408896974208
-1.2706634855419452e-08
It is quite clear that at a value of x close to 0 or 1, this function
breaks down. Yes, in "normal" use this isn't a problem, but I am looking at
fringe cases and multiplying small probabilities by very large values, in
which case the error (1e-08) becomes a large value.
Note: I have tried this with 1-x and with entering the actual number
0.00001 and 0.99999 and the accuracy issue is still there.
The questions
Firstly, is this a known problem with the qnorm and qt implementations? I
could not find anything in the documentation, the algorithm is supposed to
be accurate 16 digits for p values from 10^-314 as described in the
Algorithm AS 241 paper.
Quote from R doc for qnorm:
"Wichura, M. J. (1988) Algorithm AS 241: The percentage points of the
normal distribution. Applied Statistics, 37, 477–484.
which provides precise results up to about 16 digits."
If the R code implements the 7 digit version, why does it claim 16 digits?
Or is it "accurate" but the original algorithm is not symmetric and wrong?
If R does implement both versions of Algorithm AS 241 can I turn the 16
digit version on?
Or, is there a more accurate version of qnorm in R? Or, another solution to
my problem where I need high precision in the tails for quantile functions.
On a side note, I also have this issue with the qt distribution, at a
similar level of precision, it is not symmetric, nor precise, but I have
not investigated it yet. Also, I've posted this question on stack overflow:
http://stackoverflow.com/questions/43362644/getting-high-precision-values-from-qnorm-in-the-tail
R version:
version
platform x86_64-w64-mingw32
arch x86_64
os mingw32
system x86_64, mingw32
status
major 3
minor 3.2
year 2016
month 10
day 31
svn rev 71607
language R
version.string R version 3.3.2 (2016-10-31)
nickname Sincere Pumpkin Patch
Kind regards
Sheldon Maze
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