I'm coming from this question Similarity between two sets of random values (which still stands BTW) where @whuber suggested in the comments that I use a Q-Q plot to assess the similarity of two discrete distributions.

To recapitulate, I have two sets of random floats between [0,1] of different sizes:

$$A = \{0.3637852, 0.2330702, 0.1683102, 0.2127219, 0.0152532, ..., N_A\}$$ $$B = \{0.4541056, 0.7521812, 0.0266602, 0.5099002, 0.3468181, ..., N_B\}$$

where $N_B > N_A$.

and I need to generate the Q-Q plot for thess sets. For what I've read, if the sets where of equal sizes ($N_B = N_A$) I would simply sort both sets from minor to major values and then plot one against the other in a 1:1 correspondence.

My sets are different in sizes and the sources I've found [1], [2] both say that I need to sort both sets and then interpolate the larger set ($B$ in my case). This is counter-intuitive to me since I would expect to have to generate more points from the smaller $A$ set (through interpolation) so as to be able to plot this expanded set $A'$ against the $B$ set.

So obviously I'm not understanding correctly the process. What are the steps I should follow to generate a Q-Q plot for two distributions like the ones I presented above?

  • 1
    $\begingroup$ Most full-featured stats software does this with a single command. What software are you using? $\endgroup$
    – whuber
    Nov 27 '13 at 22:36
  • $\begingroup$ Actually, source 1 says the larger set is interpolated, not that you have to interpolate it. Confusing wording. But they mean to interpolate the larger set into the smaller. It would be easier to think of forming quantiles of each set and then plotting those. $\endgroup$
    – Peter Flom
    Nov 27 '13 at 22:43
  • $\begingroup$ I could use R but I would prefer the steps outlined separately so I can implement it in python. $\endgroup$
    – Gabriel
    Nov 27 '13 at 22:43

Generate some data:

 A <- rgamma(50,4,.1)
 B <- rnorm(30,40,20)

And then:


qq plot with interpolated A

Here I interpolated the larger set (that is, estimated 30 quantiles from 50 points); you can do the same to "interpolate" the smaller set but I don't think that helps at all, since the extra 'information' is really just a function of your quantile interpolation function.

That looks like this:

enter image description here

Here they are both plotted together (with the second plot above now having X and Y swapped so they both have the same variable on the same axis); the greater number of points has been plotted with the smaller, red circles. I think there's not really any additional information of any real value in the plot with more points, but you are free to disagree:

enter image description here

So in short, look at ?quantile and ?ppoints to see what's going on.

edit: sorry, I just noticed you have $N_B>N_A$. Mine is the other way around. I assume you can interchange the roles of A and B.

By the way, you can easily avoid sorting the sets, it's just that ppoints doesn't return things in the same order as its argument and I was being lazy.

If you define alpha=.5 then quantile(A,probs=(rank(B)-alpha)/(length(B)+1-2*alpha)) should reproduce the above plot without calling sort on either argument to plot.

(personally, I prefer values nearer alpha=.375 but not so much that I would bother fiddling with the ppoints default in most cases)

That should be enough detail that you can implement it in Python, I think.

  • $\begingroup$ Thanks again Glen, I've managed to implement this in python thanks to your thorough description. Cheers. $\endgroup$
    – Gabriel
    Nov 29 '13 at 20:44
  • 1
    $\begingroup$ I just saw your post there; I'm sorry of not thinking to just point you to the code for ppoints itself, but I wasn't thinking you would want to reproduce its behavior exactly (just the concept underlying it, which was the purpose of quantile(A,probs=(rank(B)-alpha)/(length(B)+1-2*alpha)) in my third-last paragraph there). Anyway I am glad you are happy with the outcome. $\endgroup$
    – Glen_b
    Nov 29 '13 at 21:44

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