# Excel's CHIDIST function in MATLAB for large values

This question is related to Excel's CHIDIST function in MATLAB

The minimum answer larger than zero I can get in Excel is about 2.2e-308 that correspond to realmin in MATLAB. However, in MATLAB the minimum answer is 2.2e-16, or eps(1.0). I understand this is because I first calculate chi2cdf that is close to 1 for large values, and the precision in this case is eps(1.0).

Is there any other way to reproduce Excel's CHIDIST and not to loose the precision? Do you know exactly how CHIDIST works?

EDIT:

I have to do this test multiple times, and in many cases the resulting p-values are close to zero. I need to rank the p-values, this is why I need high precision in p-value calculation.

• I am assuming that this is merely out of curiousity, as I can't imagine a "real" example where something like this matters. So if you are worried about this loss of precision for some real world problem, don't be. It is still interesting to know these sorts of things though :) Aug 6, 2011 at 18:25
• I'm unfamiliar with Matlab, but I'm confident it can calculate an incomplete Gamma integral to very high precision. See the 'upper' option at mathworks.com/help/techdoc/ref/gammainc.html.
– whuber
Aug 6, 2011 at 18:38
• @probabilityislogic: Actually I do need this precision. Later I take log of p-values, so I get many Inf values, if p-value is 0.
– yuk
Aug 7, 2011 at 2:23
• @whuber: You are right. CHI2CDF uses GAMMAINC, which in its turn takes integral from 0 to x. As I found (wiki.services.openoffice.org/wiki/Documentation/How_Tos/…) CHIDIST takes the same integral but from x to Inf. The upper option is exactly what I need. Please it as an answer.
– yuk
Aug 7, 2011 at 2:39
• @yuk - You should really add this information to the question, that what you really require is a good approximation to the log of p-value. In any event, I still struggle to see why not simply giving an arbitrarily big value whill not suffice. Your minimum value is $-16\log(2.2)$, so just take $\log(0)=-20$ or bigger Aug 7, 2011 at 3:45

Chi-square distribution functions are scaled incomplete Gamma integrals. If you compute the integral from $0$ to $x$, you approach $1$ as $x$ gets large. Subtracting this value from $1$ loses ever more precision; with double-precision arithmetic, you're limited to results greater than about $10^{-16}$. The trick is to compute the integral from $x$ to $\infty$, which typically can be approximated accurately even for extremely small values.
• Thank a lot! To be more MATLAB specific, function chi2cdf calles gamcdf, which calles gammainc. In gamcdf I've changed the line p = gammainc(z, a); to p = gammainc(z, a, 'upper');, saved it under another name and changed the call in chi2cdf. Works great!