# [R-SIG-Finance] Ranks of Spearman's rank correlation coefficient

Zanella Marco marco.zanella at inbox.com
Mon Jan 10 23:37:10 CET 2011

```Dear Gabor,
thank you for your suggestion. I've already verified the result via computation of cor() on ranked timeseries with rank(), and as you report the result is the same of cor() with spearman method on simple timeseries.

But, aren't there a way to extract the rank directly form cor() function, that of course computes ranks in order to give the result. I'm tinking in a way like "cor\$ranks".

Tanks.

Marco

> -----Original Message-----
> From: ggrothendieck at gmail.com
> Sent: Sun, 9 Jan 2011 15:02:10 -0500
> To: marco.zanella at inbox.com
> Subject: Re: [R-SIG-Finance] Ranks of Spearman's rank correlation
> coefficient
>
> On Sun, Jan 9, 2011 at 8:22 AM, Zanella Marco <marco.zanella at inbox.com>
> wrote:
>> Dear Sirs,
>> I'm using cor() funcion with Spearman method to analyse correlation of
>> timeseries. As you know Spearman method is based on ranks of timeseries,
>>
>> Well, what I'm not able to do is to extract the ranks of the timeseries
>> used from cor() to compute the Spearman correlation coefficient (also
>> called rho).
>>
>> For instance if I have this two timeseries
>>> a = c(-0.10, -0.21, -0.35, -0.38, -0.19)
>>> b = c(-0.53, -0.42, -0.59, -0.55, -0.35)
>>
>> I obtain rho in this way
>>> cor(a,b, method="spearman")
> >>[1] 0.6
>>
>> but how can I obtain ranks for the two timeseries? Of course, the same
>> computed and used by previous cor().
>>
>
> Try this:
>
>> cor(rank(a), rank(b))
> [1] 0.6
>
>
> --
> Statistics & Software Consulting
> GKX Group, GKX Associates Inc.
> tel: 1-877-GKX-GROUP
> email: ggrothendieck at gmail.com

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